Carlos J Zubiaga

Gael Guennebaud

Romain Vergne

Pascal Barla

Local Shape Editing at the Compositing Stage

: Our method permits to modify surface shape by making use of the shading and auxiliary buffers output by modern renderers. We first reconstruct shading environments for each object/material combination of the Truck scene, relying on normal and shading buffers. When normals are then modified by the compositing artist, the color image is recomposited in real-time, enabling interactive exploration. Our method reproduces inter-reflections between objects, as seen when comparing the reconstructed environments for rear and front mudguards.

Introduction

The image output by a 3D renderer prior to tone mapping may be conceived as the sum of several components (diffuse and reflection shading, emission, transparency, etc). Most modern offline rendering engines output these components in separate image buffers without impeding rendering performance. Artists may then adjust the intensity of each buffer at the compositing stage, before adding them together and applying tone mapping or color grading [START_REF] Birn | Digital Lighting and Rendering (2Nd Edition)[END_REF]. Auxiliary components such as position or normal buffers permit additional modifications: adding lights in post-process (using normals) or depth of field (using positions) for instance. This approach is most often preferred to a full re-rendering of the 3D scene that would require considerably larger assets, longer times and different artistic skills. As a result, it is routinely used in product design † e-mail:carlos.zubiaga@inria.fr applications (e.g., Colorway) or in movie production (e.g., Nuke or Flame) to quickly test alternative compositions.

However, any information about lighting is lost past the rendering stage: only shading is available in post-process. Hence if one modifies auxiliary buffers holding 3D normals or positions, it has no effect on shading buffers. One must completely re-render the scene in 3D to take such shape manipulations into account, which is a time-consuming process that forbids interactive exploration. environments per object/material: one for the diffuse term, the other for the reflection term. Obtaining new shading colors from arbitrarily modified normals then amounts to perform a pair of lookups in the respective prefiltered environment maps. Modifying local shape in real-time then becomes a matter of recompositing the reconstructed shading buffers.

Our approach is a first step toward the editing of surface shape (and more generally object appearance) at the compositing stage, which we believe is an important and challenging problem. The rendering and compositing stages are clearly separate in practice, involving different artistic skills and types of assets (i.e., 3D scenes vs render buffers). Providing compositing artists with more control over appearance will thus require specific solutions. Our paper makes the following contributions toward this goal (see Figure 1):

• Diffuse and reflection shading environments are automatically reconstructed in pre-process for each object/material combinations occurring in the reference rendering (Section 3); • The reconstructed environments are used to create new shading buffers from arbitrarily modified normals, which are recomposited in real time with the reference shading buffers (Section 4).

A simple alternative to our reconstruction approach would be to compute diffuse and reflection shading environments directly at the rendering stage. This could be done for instance by replacing each object/material combination by a sphere with the same material, roughly located at the same position. We have initially tried this approach, but it revealed to be intractable: it is not only a tedious manual process, but also requires multiple costly render passes and does not accurately reproduces inter-reflections between objects.

Related work

To the best of our knowledge, the manipulation of appearance at the compositing stage (using both shading and auxiliary buffers) has not been directly addressed in the literature. However, a number of methods are related to our objective, as detailed in the following.

Appearance manipulation

Reconstruction

In this paper, we focus on modifying the apparent shape of opaque objects at the compositing stage. We thus only consider the diffuse and reflection buffers, the latter resulting from reflections off either specular or glossy objects. These shading buffers exhibit different frequency content: diffuse shading is low-frequency, while reflection shading might contain arbitrarily high frequencies. As a result we use separate reconstruction techniques for each of them.

Both techniques take their reference shading buffer D 0 or R 0 as input, along with an auxiliary normal buffer n. Reconstruction is performed separately for each surface patch P belonging to a same object with a same material, and identified thanks to a surface ID buffer. We also take as input ambient and reflection occlusion buffers α D and α R , which identify diffuse and specular visibility respectively (see Appendix). The latter is used in the reconstruction of the reflection environment. We use Modo to export all necessary shading and auxiliary buffers, as well as camera data. The normals are transformed from world to screen-space prior to reconstruction; hence the diffuse and reflection environments output by our reconstruction techniques are also expressed in screen space.

Input diffuse layer Comparison to Least Square Reconstruction

Our QP Reconstruction

Gauss map 0 +1 -1
Figure 2: Reconstruction of the prefiltered diffuse environment map. From left to right: the pixels of the input diffuse layer of the selected object are scattered inside the Gauss map. This shading information is then approximated by low-order spherical harmonics using either a Least Square fit (top) or our Quadratic Programming formulation (bottom) leading to the respective reconstructed environment maps. To evaluate the reconstruction quality, those environment maps are then applied to the original objects, and the signed residual is shown using a color code (shown at right). Observe how our QP reconstruction guarantees a negative residual.

Diffuse environment

In this section our goal is to reconstruct a complete prefiltered diffuse environment D : S 2 → R 3 , parametrized by surface normals n. The map D should match the input diffuse buffer inside P: for each selected pixel x ∈ P, D(n(x)) should be as close as possible to D 0 (x). However, as illustrated by the Gauss map in Figure 2, this problem is highly ill-posed without additional prior. First, it is under-constrained in regions of the unit sphere not covered by the Gauss map of the imaged surface. This is due to sharp edges, occluded regions and highly curved features producing a very sparse sampling. Second, the problem might also be over-constrained in areas of the Gauss map covered by multiple sheets of the imaged surface, as they might exhibit different diffuse shading values due to global illumination (mainly occlusions).

Since diffuse shading exhibits very low frequencies, we address the coverage issue by representing D with low-order Spherical Harmonics (SH) basis functions, which have the double advantage of being globally supported and of exhibiting very good extrapolation capabilities. We classically use order-2 SH, only requiring 9 coefficients per color channel [RH01a]. The reconstructed prefiltered diffuse environment is thus expressed by:

D(n) = 2 ∑ l=0 l ∑ m=-l c l,m Y l,m (n).
(1)

The multiple-sheets issue is addressed by reconstructing a prefiltered environment as if no occlusion were present. This choice will facilitate shading edition as the local residual D 0 (x) -D(n(x)) is then directly correlated to the amount of local occlusion. Formally, it amounts to the following constrained quadradic minimization problem:

c ⋆ l,m = arg min c l,m ∑ x∈P D 0 (x) -D(n(x)) 2 , s.t. D(n(x)) ≥ D 0 (x),
which essentially says that the reconstruction should be as close as possible to the input while enforcing negative residuals. This is a standard Quadratic Programming (QP) problem that we efficiently solve using a dual iterative method [GI83].

Figure 2 compares our approach to a standard least squares (LS) reconstruction. As made clear in the two right-most columns, our QP method produces shading results more plausible than the LS method: residuals are negative by construction and essentially correspond to darkening by occlusion.

Validation The left column of Figure 6 shows reconstructed diffuse shading for a pair of environment illuminations. We project the light probes onto the SH basis and use them to render a 3D teapot model, from which we reconstruct the diffuse environments. In this case, there is no occlusion, only direct lighting: our reconstruction exhibits very good results as shown by the difference images with the ground truth environments.

Reflection renvironment

At first glance, the problem of reconstructing a prefiltered reflection environment map is similar to the diffuse case. Our goal is to reconstruct a complete prefiltered reflection environment R : S 2 → R 3 parametrized by the reflected view vector r = reflect(v, n), where v and n are the local view and normal vectors respectively. As before, R should match the input reflection buffer: for each selected pixel x ∈ P, R(r(x)) should be as close as possible to R 0 (x).

On the other hand, the reflection buffer contains arbitrarily highfrequencies, which prohibits the use of a SH representation. We thus propose to represent and store R in a high resolution dualparaboloid map [HS98] that we fill in three steps:

1. Mapping from R 0 to R while dealing with overlapping sheets; 2. Hole-filling of R using a spherical harmonic interpolation; 3. Smoothing of the remaining discontinuities.

Partial reconstruction

For the reconstruction of the diffuse map, interpreting each input pixel as a simple point sample proved to be sufficient. However, in order to reconstruct a sharp reflection map, it becomes crucial to fill the gaps between the samples with as much accuracy as possible.

To this end, we partition the input set of pixels into smooth patches according to normal and depth discontinuities. Each smooth patch has thus a continuous (i.e., hole-free) image once mapped on S 2 through the reflected directions r. Due to the regular structure of the input pixel grid, the image of the patch is composed of adjacent spherical quads and triangles (at patch boundaries). This is depicted in Figure 3 for a block of 3 × 3 pixels. Depending on object shape and camera settings, each patch may self-overlap, and the different patches can also overlap each other. In other words, a given reflection direction r might coincide with several polygons. We combine shading information coming from these different image locations using two types of weights. First, we take as input an auxiliary buffer α R storing reflection occlusions. Examples are shown in the right of Figure 7 and in the Appendix. This information tells us whether the majority of reflected rays used to compute the shading value come from the environment (i.e., other objects). This buffer can be treated as a binary mask to discard polygons exhibiting self-reflections as those polygons are unreliable to reconstruct the environment. Second, among the remaining polygons, we favor the ones that have a small extent on the unit sphere; they correspond to image locations where the object is the least curved, and hence provide the most accurate shading information. Formally, we compute a weight w k = (1-cos -1 (ℓ k )/π) η for each polygon k, with ℓ k the longest arc defined by any pair of its vertices. We use η = 200, which has the effect of selecting the smallest polygons with only a slight blend between nearly equalsized ones.

A partial reflection environment is thus obtained by combining recovered shading values for any query direction r:

R(r) = ∑ N(r) k=1 w k ∑ j∈Q k λ k j (r)R 0 (x j ) ∑ N(r) k=1 w k , (2)
where N(r) is the number of unoccluded spherical polygons con- In order to quickly recover the set of spherical polygons containing r, we propose to first warp the set S 2 of reflected directions to a single hemisphere so that the search space can be more easily indexed. To this end, we compute a rectified normal buffer n ′ such that r = reflect(z, n ′ ), where z = (0, 0, 1) T as shown in Figure 4. This is obtained by the bijection n ′ (r) = r+z r+z . In a preprocess, we build a 2D grid upon an orthographic projection of the Gauss map of these rectified screen-space normals. For each polygon corresponding to four or three connected pixels, we add its index in the cells it intersects. The intersection is carried out conservatively by computing the 2D convex hull of the projected spherical polygon. Then, for each query reflection vector r, we compute the spherical barycentric coordinates λ k j of each polygon of index k in the cell containing n ′ (r), and pickup the polygons having all λ k j positive. In our implementation, for the sake of simplicity and consistency with our 2D grid construction, we compute spherical barycentric coordinates with respect to the rectified normals n ′ , for both intersections and shading interpolation (Equation 2).

Hole-filling and regularization

Evaluating Equation 2 for each direction r in a dual-paraboloid map yields a partial reconstruction, with holes in regions not covered by a single polygon and discontinuities at the transition between different smooth parts.

For instance, the bright red region in the top row of Figure 5 correspond to reflection directions where no shading information is available (i.e., N(r) = 0). It is necessary to fill these empty regions to guarantee that shading information is available for all possible surface orientations. In practice, we perform a harmonic interpolation directly on a densely tessellated 3D sphere, with vertices indexed by r matching exactly the pixels of the dual-paraboloid map. The tessellation is thus perfectly regular except at the junction between the front and back hemispheres. We use a standard Finite Element Discretization with linear basis functions over a triangular mesh to solve the Laplacian differential equation, while setting shading values recovered by Equation 2 erly filled in, but some shading discontinuities remain. Those are caused by spatial discontinuities in Equation 2 occurring when spatially disconnected polygons are used for neighboring directions in the environment. We thus apply a last post-processing step where we slightly blur the environment along those discontinuities. We identify them by computing a second dual-paraboloid map storing the 2D image position of the polygon that contributed the respective shading color. This map is simply obtained by replacing the shading values R 0 (x j ) in Equation 2 by the 2D coordinates x j . We then compute the gradient of these maps and use its magnitude to drive a spatially-varying Gaussian blur. The effect is to smooth the radiance on discontinuities caused by two or more remote polygons projected next to one another. An example of regularized environment is shown in the bottom row of Figure 5.

Visualization Dual paraboloid maps are only used for practical storage purposes in our approach. It should be noted that once applied to a 3D object, most of the shading information in the back part of the map gets confined to the silhouette, as shown in the right column of Figure 5. In the following, we thus prefer to use shaded 3D spheres in orthographic projection (i.e., Lit Spheres [START_REF] Martin | The lit sphere: A model for capturing npr shading from art[END_REF]) to visualize the reconstructed shading environments (both diffuse and reflections). In practice, perspective projection only lets appear a subset of filled-in shading values close to object contours.

Figure 6: Reconstruction results for diffuse (left column) and reflection (right column) shading, using Galileo (top row) and rnl (bottom row) light probes. Each quadrant shows the reference prefiltered environment, along with the rendering of a 3D teapot assuming distant lighting and no shadowing or inter-reflections. The teapot image is then used to reconstruct the prefiltered environment using either the method of Section 3.1 or 3.2, and a boosted absolute color difference with the reference environment is shown.

Validation The right column of Figure 6 shows reconstruction results for a pair of known environment lighting. As before, a 3D teapot is rendered using the light probe, and then used for reconstructing reflection environment maps. The difference between our reconstruction and the ground truth is small enough to use it for shape editing.

Recompositing

The outcome of the reconstruction process is a set of SH coefficients and dual paraboloid maps for all object/material combinations appearing in the image. Obtaining reconstructed shading buffers simply amounts to evaluate shading in the appropriate SH basis or environment map, using arbitrary screen-space normals. A benefit of this approach is that any normal manipulation may then be used in post-process; we give some practical examples in Section 5.

However, we must also ensure that the independently reconstructed shading buffers are seamlessly recombined in the final image. In particular, when a normal is left untouched by the compositing artist, we must guarantee that we reproduce the reference diffuse and reflection shading colors exactly. This is the goal of the recompositing process: taking as input an arbitrarily modified normal buffer, it combines the reconstructed prefiltered environments with rendered input buffers to produce a final color image where the apparent shape of objects has been altered. It works in parallel on all pixels; hence we drop the dependence on x for brevity.

Combined diffuse term Given a modified normal ñ, we define the combined diffuse term D by:

D = α D D 0 -D(n) residual +D( ñ) + (1 -α D ) D 0 , (3)
where the ambient occlusion term α D is used to linearly interpolate between the reference and reconstructed diffuse colors. The rationale is that highly occluded areas should be preserved to prevent the introduction of unnatural shading variations.

The D 0 -D(n) term is used to re-introduce residual differences between the reference and reconstructed buffers. It corresponds to local darkening of diffuse shading that could not be captured by our global reconstruction. The ⌊•⌋ symbol denotes clamping to 0, which is necessary to avoid negative diffuse shading values. This is still preferable to a multiplicative residual term D 0 /D(n) as it would raise numerical issues when D(n) ≈ 0. Observe that if n = ñ then D = D 0 : the reference diffuse shading is exactly recovered when the normal is not modified.

Combined reflection term Contrary to the diffuse case, we cannot apply the residual approach between the reference and reconstructed reflection buffers as it would create ghosting artifacts. This is because reflections are not bound to low-frequencies as in the diffuse case. Instead, given a modified normal ñ and a corresponding modified reflection vector r = reflect(v, ñ), we define the combined reflection term R by:

R = ν r,r α R R(r) + (1 -ν r,r α R ) R 0 , (4)
where ν r,r = min(1, cos -1 (r • r)/ε) computes the misalignment between original and modified reflection vectors (we use ε = 0.005π), and α R is the reflection occlusion term. The latter serves the same purpose as the ambient occlusion term for the diffuse case. Equation 4 performs a linear interpolation between reference and reconstructed reflection colors based on ν r,r α R . As a result, if n = ñ, then ν r,r = 0 and R = R 0 : the reference reflection shading is exactly recovered when the normal is left unmodified.

Final composition

The final image intensity Ĩ is given by:

Ĩ = α k D D + k R R 1 γ + (1 -α) I, (5)
where the diffuse and reflection coefficients k D and k R are used to edit the corresponding shading term contributions (k D = k R = 1 is the default), γ is used for gamma correction (we use γ = 2.2 in all our results), and α identifies the pixels pertaining to the background (e.g., showing an environment map), which are already gamma corrected in our input color image I.

Equation 5 is carried out on all color channels separately. In particular, it permits to seamlessly reproduce D 0 outside of the square region (e.g., inside the ear). Similarly, using Equation 4 permits to remove implausible bright reflections in reflection shading (e.g., inside the ear or below the eyebrow).

Experimental results

We have implemented the recompositing process of Section 4 in Gratin [VB15], a programmable node-based system working on the GPU. It permits to test various normal modification algorithms in 2D by programming them directly in GLSL, while observing results in real-time as demonstrated in the supplemental video. Alternatively, normal variations can be mapped onto 3D objects and rendered as additional auxiliary buffers at a negligible fraction of the total rendering time. It then grants compositing artists the ability to test and combine different variants of local shape details in post-process. We demonstrate both the 2D and 3D normal editing techniques in a set of test scenes rendered in global illumination.

We start with three simple 3D scenes, each showing a different object with a same material in a same environment illumination. The normal buffer and global illumination rendering for each of these scenes are shown in the first two columns of Figure 8. Diffuse and reflection environments are reconstructed from these input images and shown in the last two columns, using Lit Spheres. The reconstructed diffuse environments are nearly identical for all three objects. However, the quality of reconstruction for the reflection environment depends on object shape. The sphere object serves as

Alternative normals

Our approach Ground truth

Figure 9: A Voronoi-based bump texture is mapped onto the red head and vase models in 3D, yielding an alternative normal buffer. Our approach is used to modify shading at the compositing stage in real-time, with a resulting appearance similar to re-rendering the object using the bump map (ground truth).

a reference and only differs from the LitSphere view due to perspective projection. With increasing shape complexity, in particular when highly curved object features are available, the reflection environment becomes less sharp. However, this is usually not an issue when we apply the reconstructed environment to the same object, as shown in Figures 9 and10.

We evaluate the visual quality of our approach on the head and vase objects in Figure 9. The alternative normal buffer is obtained by applying a Voronoi-based bump map on the object in 3D. We use the reconstructed environments of Figure 8 and our recompositing pipeline to modify shading buffers in the middle column. The result is visually similar to a re-rendering of the scene using the additional bump map, shown in the right column. A clear benefit of our approach is that it runs in real-time independently of the rendered scene complexity. In contrast, re-rendering takes from several seconds to several minutes depending on the scene complexity.

Figure 10 demonstrates three interactive local shape editing tools that act on a normal n = (nx, ny, nz). Normal embossing is inspired from the LUMO system [Joh02]: it replaces the nz coordinate with β nz where β ∈ (0, 1] and renormalize the result to make the surface appear to "bulge". Bump mapping perturbs the normal buffer with an arbitrary height map, here a fading 2D ripple pattern (the same manipulation is applied in Figure 7 with a 2D Perlin noise). Bilateral smoothing works on projected normals n = (nx, ny) using an auxiliary depth buffer to preserve object contours.

In more complex scenes, images of objects may appear in the reflections of each other. This is what occurs in Figure 11, which shows a noise texture on the cup, an embossed symbol on the kettle body and a carved pattern on the vase. The results remain plausible even when the material properties are edited, as shown in the right column where we decrease the diffuse intensity and increase the specular intensity. The reconstructed diffuse and reflection environments are shown combined in Figure 12, before and after material editing has been performed. Observe in particular how the reflections of nearby objects have been properly reconstructed. The reflected window appears stretched in the cup environment. This is because it maps to the highly curved rim of the cup. However, when reapplied to the same object, stretching goes unnoticed.

The Truck scene of Figure 1 is more challenging: not only object parts are in contact, but each cover a relatively small subset of surface orientations. Nevertheless, as shown in the reconstructed shading environments, our method manages to capture a convincing appearance that reproduces inter-reflections between different parts. This permits to generate a plausible result when normals are modified to apply a noise texture and an embossed emblem to the truck body, and corrugations to the front and rear mudguards.

Performance Reconstruction timings for all edited objects in the paper are given in Table 1, using a single CPU core on an i7-4790k@4.00GHz. The reconstruction of the diffuse environment is negligible compared to that of the reflection environment. Our partial reconstruction could be easily optimized with a parallel implementation. The performance of the hole-filling process highly depends on the size of the hole; it could be greatly improved by using an adaptive sphere tesselation strategy. Nevertheless, reconstruction is not too performance-demanding as it is done only once in pre-process. Most importantly, recompositing works in real-time in all our examples.

Discussion and future work

We have demonstrated a first solution for the edition of surface shape at the compositing stage, based on environment reconstruction and real-time recompositing. Our solution is tailored to opaque objects. We have demonstrated it on homogeneous materials, but it would be easily extended to objects with spatially varying reflectances, provided that diffuse (resp. reflection) illumination and reflectance buffers are output separately by the renderer. Our method would then be used to reconstruct the diffuse (resp. reflection) illumination buffers, and shading would be obtained through multiplication by reflectance at the recompositing stage. However, our method would not readily work with transparent or translucent objects, since the complex light paths inside object volumes would forbid the reconstruction of prefiltered environment maps. Our approach also explicitly discards inter-reflections on a same object using occlusion buffers, but deals with inter-reflections among different objects as long as they are not too much extended in the scene. For instance, if the rear and front mudguards of Figure 1 where connected together, a single pre-filtered shading environment would not be enough. To deal with this issue, we would like to explore reconstructing multiple shading environments for extended objects and recombining them through warping.

Original/modified normals Before/after shape editing With edited material Reconstruction is also restricted by object curvatures. If object shape is too simple, it will not provide enough shading information, which will require to fill in wider regions. This is illustrated in Figure 13, which shows the reconstructed reflection environment for the spoon in the Table top scene of Figure 11. In contrast, Reconstructed env. After hole-filling After regularization Figure 13: The reconstructed reflection environment for the spoon object in Figure 11 does not contain enough shading information to grant editing, even after hole-filling and regularization.

if object shape is too detailed with respect to image resolution, it will tend to reduce the accuracy of the reconstructed shading, as seen in the right column of Figure 8 when object complexity is increased. Nevertheless, as demonstrated in Section 5, our approach gives satisfying results in practical cases of interest, granting interactive exploration of local shape variations with real-time feedback. We believe it could already be useful to greatly shorten trial and error decisions in product design and movie production. A natural next step will be to manipulate surface positions instead of normals, in effect modifying shading buffers as if displacement maps were applied to objects. Our current approach does not permit to reproduce the associated visibility effects, such as cast shadows, local inter-reflections and modified silhouettes.

In this paper, we have considered out-of-the-box render buffers and have focused on algorithms working at the compositing stage. In future work, we would also like to work at the rendering stage to export more detailed shading information while retaining a negligible impact on rendering performance.

Figure 3 :

 3 Figure 3: A 3 × 3 pixel neighborhood (shown at left on a lowresolution image for clarity) is mapped to four contiguous spherical quads on the Gauss sphere of rectified normals (right). The color inside each spherical quad is computed by bilinear interpolation inside the input image using spherical barycentric coordinates.

Figure 4 :

 4 Figure 4: A normal n is "rectified" to n ′ prior to reconstruction. The reflection of the view vector v is then given by r = reflect(z, n ′ ). taining r, Q k is the set of corner indices of the k-th polygon, and λ k j are barycentric coordinates enabling the interpolation of the shading colors inside the k-th polygon. For polygons embedded on a sphere, barycentric coordinates can be computed as described in Equation 8 of [LBS06]. We use normalized spherical coordinates, which amounts to favor the partition of unity property over the linear precision property on the sphere (i.e., Equation 2 instead of 3 in [LBS06]).

  as Dirichlet boundary constraints [IGG * 14]. A result is shown in the middle row of Figure 5: holes are propc 2016 The Author(s) Eurographics Proceedings c 2016 The Eurographics Association.

Figure 5 :

 5 Figure 5: A dual paraboloid map reconstructed with our approach is shown in the top row: it only partially covers the space of reflected view vectors. The missing information, shown in red, appears confined to the silhouette when visualized with a LitSphere at right. We focus on the back part of the paraboloid map in the middle row to show the result of the hole-filling procedure. Missing regions are completed, but some discontinuities remain; they are corrected by our regularization pass as shown in the bottom row.

Figure 7 :

 7 Figure 7: Illustration of the different terms involved in recompositing (Equations 3-5). The left column shows reference and modified images, where we have perturbed the normal buffer with a 2D perlin noise in a square region. This permits to show the behavior of our approach on both modified & unmodified portions of the image (i.e., inside & outside the square); in the latter case, Ĩ = I. The remaining columns present the diffuse (top) and reflection (bottom) terms. Starting with the reference shading at left, we then show reconstructed shading using prefiltered environments. Reference and reconstructed shading terms slightly differ in some unmodified regions (e.g., inside the ear). The combined shading terms correct these effects with the aid of ambient and reflection occlusion buffers, shown in the rightmost column.

  Figure 7 shows an example of the recompositing process on a simple scene holding a single object, where input normals have been corrupted by a 2D Perlin noise restricted to a square region. The final colors using original and modified normals are shown in the leftc 2016 The Author(s) Eurographics Proceedings c 2016 The Eurographics Association. Normals Reference Diffuse env. Reflection env.

Figure 8 :

 8 Figure 8: Reconstructed diffuse and reflection prefiltered environment maps for three objects: the red sphere (top), head (middle) and vase (bottom) models. The diffuse environment reconstructions are nearly identical in all three cases. The sharpness of the reflection environment reconstructions vary with object shape.

Figure 11 :

 11 Figure 11: The left column shows the original normal buffer of the Table top scene (top), as well as a modified version (bottom) where a noise texture, an embossed symbol and a carved pattern have been respectively applied to the cup, kettle body and vase. In the middle column, we show the result of our approach (bottom) on the corresponding color image (top), using the reconstructed diffuse and reflection environments shown in Figure 12 (1st row). In the right column, we have edited the intensities of the diffuse and reflection components inboth the original and modified scene. Reflections of nearby objects become more clearly apparent, as is also seen in Figure12(2nd row).

Figure 12 :

 12 Figure 12: Combined diffuse and reflection environments reconstructed from the cup, kettle and vase objects of Figure 11. The bottom row shows edited materials where the diffuse term is divided by 4 and the reflection term multiplied by 4: the supporting table and other nearby objects appear more clearly in the reflections.

  Lighting reconstruction Another approach would be to reconstruct scene lighting at the compositing stage.Lombardi et al. [LN12] tackle a similar problem: estimating both reflectance and natural illumination from a single image of an object of known shape. The reconstructed lighting environments exhibit artifacts, but these occur only when re-rendering the object with a shinier material compared to the original one. Since material properties can be made available in auxiliary buffers, this method could yield more accurate results in a compositing context. However, as shown by Ramamoorthi et al.[RH01b], there are inherent limitations on the recoverable lighting frequency content depending on the material considered. Most importantly, it is not necessary to decouple material and lighting for local shape editing: recovering a prefiltered lighting [HS98] is enough. This approach has been taken by Zubiaga et al. [ZMB* 15] to decompose images of shaded spheres (called MatCaps or Lit Spheres[START_REF] Martin | The lit sphere: A model for capturing npr shading from art[END_REF]) into diffuse and reflection environment maps. Our work seeks a similar reconstruction, with the added difficulties that object shape is arbitrary (not just a sphere), with multiple objects rendered in global illumination.

	15]), both methods being employed to reduce render-
	ing costs. Here buffers such as positions, normals and surface IDs
	are used to select which pixels are likely to reflect similar radiance
	in a small neighborhood around a pixel of interest. Recent methods
	such as the work of Tokuyoshi et al. [Tok15] also adapt filtering to
	material properties. Another use of auxiliary buffers is for novel-view synthesis [LRR * 14]: positions, normals and motion flows are
	used to interpolate between two rendered images. Contrary to these
	methods, our approach requires to use shading colors in very large
	neighborhoods around the pixel of interest (potentially the whole
	image) to recover as much shading information as possible.
	method takes
	inspiration on paintings to manipulate reflections off mirror-like
	surfaces in non-physical ways. However, it works entirely in 3D
	space and would not be easily adapted to work at the composit-
	ing stage. The surface flows technique [VBFG12] does work exclu-
	sively from normal and depth (i.e., auxiliary) buffers. However, the
	goal of the method is rather to create 3D shape impressions using

The image-based material editing technique of Khan et al.

[START_REF] Khan | Image-based material editing[END_REF]

addresses the difficult problem of manipulating material appearance given a single input image. It mostly relies on an estimation of approximate depth buffer and environment illumination, which are used in a potentially costly rerendering pass using different materials. Despite the simple estimation heuristics used, results are most often convincing: the human visual system can tolerate quite large discrepancies in physical accuracy [RFWB07]. This is also exemplified in the work of Ritschel et al. [ROTS09] on interactive reflection editing. The normal-and depth-based image deformations. Instead, our goal is to manipulate existing shading buffers based on modified normals; this is similar to bump mapping [Bli78], but at the compositing stage. As in previous work, our method relies on the tolerance to physical inaccuracies exhibited by human vision.

2.5D image synthesis

Previous work has made use of auxiliary buffers with different goals than local shape editing. A common use is for the filtering of noise resulting from Monte Carlo rendering (e.g., [RMZ13]), as well as upsampling to higher resolutions (e.g., [ZRJ *

Table 1 :

 1 Table top scene with various objects: a cup made of porcelain with a metal spoon, a reddish coated kettle with an aluminum handle, and a vase made of a glossy material exhibiting blurry reflections. Despite the increased complexity, our method still produces a plausible result when normals are modified with a Our technique permits to apply arbitrary modifications to the normal buffer, while still yielding plausible shading results. Normal embossing is applied to the eyebrow of the head, and the whole vase, resulting in an apparent bulging of local shape. Any bump texture may be applied as a decal to modify normals: we use a 2D fading ripple pattern, affecting both diffuse and reflection shading. Local shape details may also be removed: we apply a cross bilateral filter on normals, using an auxiliary depth buffer to preserve occluding contours. Timings in ms for each reconstruction stage.

	Normal embossing		Bump mapping	Bilateral smoothing
	Figure 10: Model	Diffuse		Reflection
		rec.	Partial rec. Hole filling
	Red sphere	110	315	215
	Red head	184	470	280
	Red vase	147	385	192
	Cup	29	45	325
	Kettle	65	97	650
	Black vase	64	133	4000
	Truck body	119	120	6000
	Front mudguard	25	45	2500
	Rear mudguard	14	26	3700

c 2016 The Author(s) Eurographics Proceedings c 2016 The Eurographics Association.

Acknowledgements

This work was supported by the EU Marie Curie Initial Training Network PRISM (FP7 -PEOPLE-2012-ITN, Grant Agreement: 316746) to C.J. Zubiaga and P. Barla. The authors would like to thank The Foundry for providing MODO licenses and assets (objects and environment maps).

Appendix

For completeness, we present from left to right the surface IDs, ambient and specular occlusion buffers, first for the Table top scene, then for the Truck scene.


Agustinus Prasetyantoko
email: aprasety@ens-lsh.fr

Financing Constraint and Firm Investment Following a Financial Crisis in Indonesia ℑ

Keywords: asymmetric financing opportunities, financing constraint, firm Investment, financial crisis JEL Classification: D2, E51, G31

This paper deals with the sensitivity relation between firm-level investment and its internal liquidity by splitting samples into two different groups of firms, which are tradable (T) and non-tradable (N) sector. The study includes 226 listed companies in Jakarta Stock Exchange (JSX) by at least five consecutive years in the period of 1994 -2004. This paper finds that during boom period, N-sector is less financially constrained, but in burst period, N-sector has greater financial constraints. It leads us to the explanation that during boom period N-sector grows faster than T-sector, but when crisis hits T-sector recovers more easily. By employing panel data analysis, our findings support an argument that asymmetric financing opportunities among N and T-sector are common in developing countries. Accordingly, this paper provides important explanations on firm-level investment behavior around financial crisis, which could be pivotal considerations in monetary and other relevant policies.

Introduction

From academic point of view, the 1997 Asian crisis is a challenging "puzzle" inspiring series of research agenda in many fields of studies. Studies on both, theoretical and empirical perspectives are employed to understand this fashionable generation of crisis. Even though, research on it is still far from exhaustive since intricate problems intervene. This paper proposes a piece of explanation centered on the micro evidence of the macro economic fragility rising from the asymmetric financing opportunities between tradable (T) and non-tradable (N) sector in Indonesia. For this issue, Tornell and Westermann (2002) have an interesting explanation as follows, "Many countries that have liberalized their financial markets, have witnessed the development of lending booms that sometimes ends in twin currency and banking crises and is followed by a protracted credit crunch that outlives a short-lived recession". Due to financial crisis, they identify two pivotal problems as main sources, namely risky currency mismatch and asymmetric financing opportunities across the T-sector and N-sector. This paper deals with the latter issue, which is the asymmetric financing opportunity among T and N-sector at the onset and aftermath of crisis. Related to this issue, [START_REF] Tornell | The positive link between financial liberalization, growth and crisis[END_REF]Westermann (2002a, 200b, 2004) give further explanation of the coincidence of the credit market imperfections and asymmetric sector development as a common phenomenon in the middle income countries (MICs) that lead into financial fragility. For concerned countries, in the period of lending booms, that sometimes ends in currency and banking crisis, the N-sector grows faster that T-sector but inversely N-sector recovers slower than T-sector in the period of credit crunch following a financial crisis1 .

In line with this explanation, this paper has a fairly simple question: whether N-sector has more financial constraint than T-sector following a financial crisis in Indonesia. This question will be addressed by examining firm-level investment sensitivity with its internal liquidity or cash flows by grouping samples into two different parties2 . The findings of this empirical study should be important in explaining the macro economic fragility, since firmlevel investment is a pivotal variable in macro economic fluctuation.

Financial Crisis and Firm Investment in Indonesia

Indonesia is a country undergoing a relatively slow economic recovery from depth and width of crisis. In 2004, compared with other countries in South East Asian region, real GDP growth in Indonesia was relatively lower: Indonesia had 4.9 % real GDP growth, meanwhile Thailand had 6.2 %, Philippines 6.0 % and Malaysia 7.1 %3 . The data released by The World Bank shows that following the 1997 shock economic performance in Indonesia was weaker than average performance of East Asian countries. In Q-4 2001, Indonesian real GDP growth was 1.0 %, whereas average growth of East Asian countries was 4.3%.

After having high economic growth during over one decade, Indonesia faced great serious turbulence on economic performance4 . The dismal performance of Indonesia's economy was started by the presence of extreme shock due to the 1997 financial crisis.

Indonesia is a country suffering a deep crisis, where GDP growth reached minus (-) 13 % and inflation reached 58.5 % in the end of 1998. Meanwhile, to cope with the high currency depreciation, monetary authority in Indonesia (Bank Indonesia) hiked interest rate into 70.44 % on August 1998 5 . In such condition, there were no firms that can continue their activities normally. Approximately a half of Indonesian corporations became technically insolvent due to currency depreciation.

Theoretically, monetary authority is generally able to affect corporate sector through multiple channels. Bernanke and Gertler (1995) differentiate: balance sheet channel and bank lending channel. Balance sheet channel focus on the impact of the monetary policy on the borrowers' balance sheet through firm net worth, cash flow and liquid assets. Meanwhile bank lending channel describe the influence of monetary policy by the changing of the supply of loans channeled by banking institutions.

In emerging countries, bank credit plays a very important role in firm expansion, since capital market institutions are considerably underdeveloped. In this condition monetary policy directly imposes corporate sector healthiness. By the high rate of interest, money supply from banking institution should decrease significantly. In such condition, corporate sector could not pay the interest rate. In the same time, due to financial difficulties corporate sector undergoes worse balance sheet due to the deteriorating of debt-equity ratio as well as internal liquidity.

In Indonesia, following financial crisis in 1997, corporate sector perform poorly, where investment level was very low. Instead of expanding their investment, firms prefer to consolidate their activities first. Following financial shock, the poor performance of firm-level investment was strongly impacted by tight money policy applied by Bank Indonesia dealing with exchange rate volatility 6 . However, it is not single factor influencing the gloomy condition.

In July 1999, due to the strengthening of economic condition, Bank Indonesia downgraded the interest rate into 13.8 % (see chart 1 in appendix). Generally, after July 1999 Indonesia can release from the period of crisis. It is due to that macro economic conditions were relatively stable, in term of inflation, exchange rate and interest rate. Nevertheless, the decrease of interest rate failed to support the real sector recovery. It seems that supply of credit from banking sector was not channeled into real sector. The question is whether it was caused by the inability of the real sector to absorb credit supply or rather the unwillingness of banking sectors to offer their credit.

In study conducted by Bank Indonesia, it was confirmed that credit crunch was present following a financial crisis in Indonesia 7 . Banking sector preferred to not offer their credit to firms sector to avoid the borrower risk-averse behavior. Theoretically, the lack of credit supply to firms sector could generate a second round effect on the business failures, which could also exacerbate the quality of bank loans. In such condition, there is a risk that the collapse of real sector could attack banking crisis in the second round. Hence, there is a complicated vicious circle which can not be easily resolved. 6 Tight money policy employed by Bank Indonesia become a central debate until nowadays, since this policy is required by International Monetary Fund (IMF) who has a fallacy in his policies in line with really happen in Asian countries in the mid of crisis. For this issue, see Iwan Jaya Moreover, firm with weak financial condition tend to carry out financial consolidation first rather than employ business expansion. Meanwhile, firms Firm with bad balance sheet could not pay their maturity debt to the bank. In this moment, the deterioration of firm net worth induces directly to the balance sheet of banking sector.

Following a financial crisis, firms commonly prefer to reduce their activities by postponing their loan from banking sector. Instead of expansion, firms prefer to consolidate their internal activities in operation by reducing their internal liquidity and inventories rather than borrow from bank. In this case, demand for working capital credit diminishes significantly. Add up with the uncertainties in economic and business condition due to financial crisis, firms prefer to use their own capital rather that borrow from bank with high interest rate.

Bank Indonesia also finds that firms prefer to use their own fund (retained earnings) as the main source of the finance in the aftermath of crisis8 . As amount of 56% from respondents in this survey use the retained earning, meanwhile 44% use the external fund of which the majority still originates from the bank credit, namely around 24% comprising 14% working capital credit and 10% investment credit. Others source of external financing are the capital market (6%), offshore loans (5%), bonds (3%), and their own group (1%)9 .

Balance sheet effect mechanism attacks simultaneously firm and banking sector. This mechanism commonly happens in country with weak corporate and banking sector, where third generation of crisis is present. In Mexico crisis has decreased GDP in the last three quarters of 1995 from 9.2% into -8% and 7%. However, Mexico had better chance than Indonesia since economic condition could be immediately recovered. In the second quarter of 1996, GDP growth grew at an average of above 5% until the first quarter of 199810 .

In Mexico, asymmetric financing constraint was also present, where in post crisis period, tradable sector grow and recover easily, whereas non-tradable sector face stagnation following crisis and the presence of credit crunch. Although, some researches predict that usually tradable sector is able to get funding from the international market, but non-tradable sector must depend on own financing since bank is reluctant to provide credit for the latter sector11 .

Empirical Research

To deal with the question of which sector performs better (T or N) in before and postcrisis period, this paper employs the relatively rigorous equation measuring the sensitivity of the firm-level investment and its liquidity.

Investment equation

Since a seminal paper of Fazzari, Hubbard and Petersen/FHP (1988), the issue of financing constraints and firms investment have been popular debates among scholars12 . FHP show that firms which are identified a priori as financially constrained have greater sensitivity in investment to the availability of internal finance in term of cash flow. In their proposition on financing constraint paradigm, they claim that the sensitivity of investment and liquidity is driven by the presence of the asymmetric information in capital market. This argument is substantially different from neoclassical perspective on investment such as Modigliani and Miller/MM (1958) who promote the irrelevance of financial structure theory explaining that financial policy is not relevant for real investment decisions under certain conditions 13 . Oppositely, FHP propose the theoretical models of imperfections in capital market implying that external financing is more costly than internal financing for many firms. Since the degree of asymmetric information and agency costs depends on firm characteristics, certain firms may be more sensitive to financial factors than others. In other words, industrial or individual characteristics of the firms become important determinants of investment sensitivity to internal finance (cash flow).

Under the argument of FHP, investment should be significantly related to proxies for changes in net worth or internal fund. As in many previous studies, this paper uses the ratio of cash flow to the capital stock (CF/K) as the internal financing condition of the firms. A large body of research has found that the sensitivities of investment on its cash flow are higher for financially constrained firms (FHP, 1998;[START_REF] Chirinko | Further Evidence On The Relationship Between Firm Investment And Financial Status[END_REF].

To provide empirical evidence, this paper uses the basic equation originally developed by FHP (1988) as follows:

I = f(investment opportunities) + g(Internal funds) or (1) ( ) ( ) it it it it it it it K CF g K X f K I υ + + = - - - 1 1 1
where I it represents investment in fixed assets for firm i during period t, X represents a vector of variables, and u is an error term for i and t.

Following FHP (1988), the function g depends on the firm internal cash flow (CF), which represents the potential sensitivity of investment, to fluctuations in available internal finance, after investment opportunities are controlled for through the variables in X. All variables are divided by the capital stock in the beginning of period (K t-1 ).

It is commonly applied the sensitivity of internal capital and investment into different characteristic of firm, such as low and high of dividend payout rate (Fazzari et al., 1998), Keiretsu or independent firms [START_REF] Hoshi | Corporate structure, liquidity, and investment: evidence from Japanese industrial groups[END_REF], bond rating [START_REF] Whited | Debt, liquidity constraints, and corporate investment: evidence from panel data[END_REF], and tradable or non-tradable sector [START_REF] Espanol | Why exportable firms can be financially constrained in a recently liberalized economy? A puzzle based[END_REF]. Since the interest of this research resides on the asymmetric sectoral development, this paper runs regression for different firm categories, which are Tradable (T) and Non-tradable (N) sector. This paper use fixed asset as a proxy of long-term investment. For capturing the sensitivity of T and N-sector, this paper employs the following equation ( 2

) (2) ( ) ( ) ( ) ( ) ( ) it it it d it it wk it it it q it it cfn it it cft it it K D K WK K S Q N K CF T K CF K I υ α α α + + ∆ + + + + = - - - - - - - 1 1 1 1 s 1 1 1 α α α * * where K = Fixed-asset I = Investment in long-term or gross investment (K t -K t-1 ) CF = Cash Flow Q = Tobin's Q S = Total Sales ∆WK = Change of Working Capital (Current Assets -Current Liabilities) D = Total Debt T = Tradable sector N = Non-tradable sector
For robustness check, this paper employs several regression procedures. First, running estimation by excluding proxies of profitability and market value of the firms, which are lag of sales and Tobin Q.

(3)

( ) ( ) ( ) ( ) it it it d it it wk it it cfn it it cft it it K D K WK N K CF T K CF K I υ α α + + ∆ + + = - - - - - 1 1 1 1 1 α α * *
Other check of robustness is employed by considering the industrial sector effects. To do this, this paper uses equation where dummies of industrial sectors are introduced. By this equation, this paper focuses on the effects of industrial sector on the relation between firm investment and liquidity. For this purpose, this paper does not split sample on two different groups as be done in previous equation or equation (2), rather than applying dummies for each sector by equation (4) as follows:

(4) ( ) ( ) ( ) ( ) it j it it d it it wk it it it q it it cf it it K D K WK K S Q K CF K I ε η α α + + + ∆ + + + = - - - - - - 1 1 1 1 s 1 1 α α α
Industrial sector dummies 14 1. Sector A (basic industry) = agriculture (sector 1), mining (sector 2).

2. Sector B (manufacturing) = basic industry & chemical (sector 3), miscellaneous industry (sector 4), consumer good industry (sector 5) 14 For sector dummies we use JASICA or Sectoral Index classification which is launched by Jakarta Stock Exchange authority on January 1996

Variables

Dependent Variable

Investment

In common understanding, firm-level investment is the value of machinery, plants, and buildings that are bought by firms for production purposes. Accordingly, this paper use fixedasset as proxy of investment in long-term period. Theoretical prediction considers that constrained firms should display a stronger sensitivity of investment to cash flow (FHP, 1998;[START_REF] Chirinko | Further Evidence On The Relationship Between Firm Investment And Financial Status[END_REF][START_REF] Bruinshoofd | Corporate investment and financing constraints: connections with cash management[END_REF]. In this case, if MNC face larger financial constraints than firms from DC, it should be expected that α cfMNC to be higher than α cfDC.

Independent Variables

Cash-Flow

In [START_REF] Fazzari | Cash flow, investment, and Keynes-Minsky cycles[END_REF] we find that in the Keynesian endogenous investment model, if cash flow is insufficient to finance investment, firms take on debt. The implication is that investment activities should be financed primarily by internal finance. In this case, generally cash flow should be negatively related to firm investment.

Recently, a large body of literature suggesting that because of information asymmetries and capital market imperfections, corporate investment expenditure are significantly influenced by internal ability of firms to generate internal cash. It leads to the explanation that firm prefer first to the internal equity rather than external debt, so that investment should be negatively related to debt.

Tobin's Q

Tobin's Q is associated with firm's market capitalization reflecting the market anticipations of firm profitability investment opportunities. Tobin Q is measured by market value of assets deflated by book value of assets. In this paper, α q is expected to be significant and positive.

Profitability

In this paper, sales in previous period are used for the proxy of profitability. In common sense, profitability will increase with investment. We expect that profitability will be significant and positive. Profitability is considered to explain the past and potential future performance of a firm. It is consistent with sales accelerator model; higher level of sales will enhance the production capacity in order to meet an enlarged demand (FHP, 1988) [START_REF] Fazzari | Working capital and fixed investment: new evidence on financing constraints[END_REF] describe that working capital is current assets (chiefly accounts receivable, inventories and cash) less current liabilities (primarily accounts payable and short-term debt), and it measures the firm's net position in liquid assets. Due to financial constraint, [START_REF] Fazzari | Working capital and fixed investment: new evidence on financing constraints[END_REF] argue that it is costly for firms to change the level of fixed investment, and thus they will seek to maintain a stable fixed-investment path, over things equal by adjusting working capital. This argument is comparable with internal net works hypothesis (Bernanke and Gertler, 1989).

Working Capital

External finance, if available, may be more costly than internal finance because of transaction costs, agency problems, or asymmetric information. Thus, other thing equal, when firms choose to decrease (increase) working capital investment, fix investment should rise (fall).

In this case, α wk is expected to be significant and negative.

Debt

There are two opposite theoretical analysis about the relation between firm's leverage and cash flow. Trade off theory suggests a positive relationship [START_REF] Modigliani | The Cost of Capital, Corporation Finance and the Theory of Investment[END_REF], while pecking order behavior implies a negative relationship [START_REF] Myers | Corporate Financing and Investment Decisions when Firms have Information that Investors Do Not Have[END_REF]. Meanwhile, signaling theory suggests that a higher debt ratio can be considered as a signal of an improved capacity to finance investment, and hence the relation between debt and investment will be positive.

Results

Data set and Univariate Analysis

For this study, we include all non-financial firms listed in the Jakarta Stock Exchange (JSX) by using yearly accounting data provided by JSX's database and Indonesian Capital Market Directory (ICMD) provided by ECFIN, a private company. We have 298 samples of all non-financial firms listed in various different time period. In both data base, we do not find the variable of cash flow. Accordingly, we accessed directly to the annual report of each firms documented manually in JSX. In this case, we work manually to input data.

We sort data with at least 5 consecutive years and we find 234 firms. Furthermore, we exclude the outliers by sorting data from their median and standard deviation. We decide to exclude data with more than 10 percent of standard deviation. And finally, we have 226 firms for the period of 1994 -2004. For classification of T and N-sector, we follow the JSX's category.

In graph 1, we can see how investment fluctuates during the period of study (1995 -2004) 16 . We find that on the onset of crisis (1995 -1996) both sector (T and N) were actually declining. They (both sectors) respond comparably the 1997 financial crisis, where the rate of investment has dropped drastically in the aftermath of crisis (1998 -1999). From this graph, it is actually difficult to analyze which sector grew faster before crisis and sector recover quicker in the post crisis. The median of the investment rate among both sectors have comparable tendency.

For examining the different investment level between T and N sector, this paper employs two tests of variances, which are t-test for mean variance (t-statistic) and Wilcoxon rank-sum (Mann-Whitney) test for median variance (z-statistic). And we can seen in table 1 (in appendix) that there is no significant different of the firm-level investment between both sectors. 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 T N

Graph 1. Median of Firm-level Investment over Capital Stock (

1 - it it K I ) Investment -0,2 0 0,2 0,4 0,6 0,8 1 1,2
Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Note: T is tradable sector, N is non-tradable sector

In term of cash flow volatility or firm liquidity such as described in graph 2, there are no significant differences between tradable and non-tradable sector. This observation is valid since tests of variance show there are no such significant differences between tradable and non-tradable sector in term of cash flow. 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 T N Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Note: T is tradable sector, N is non-tradable sector Tobin Q as a proxy of firm opportunity is also not significant different in both sectors.

The graph 3 shows a slight difference of Tobin Q in both sectors. However by the test of variance it is evident that the both sectors have no significant differences in Tobin Q. Tests of variance show that N sector has higher mean variance of Tobin Q in 0.2389 as well as higher median variance in 0.556. But both tests for t-statistic and z-statistic are not significant. 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 T N

Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Note: T is tradable sector, N is non-tradable sector

From the following graph (graph 4), it seems that T-sector has much higher level of sales. Test of variance also shows a significant difference in 1 percent for t-statistic and 5 percent for z-statistic. However, there is dubious evidence, since t-test which measure the difference with mean shows that N-sector has higher sales (6.406) meanwhile Wilcoxon ranksum (Mann-Whitney) test inversely show that N-sector has smaller sales (62.091). It is showed that the variance of firm's sales is relatively high. 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 T N

Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Note: T is tradable sector, N is non-tradable sector

In term of working capital, graph 5 shows how T and N-sector move comparably. And this evidence is supported by t-statistic and z-statistic, which show that there is no such significant difference in working capital between T and N-sectors. 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 T N

Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Note: T is tradable sector, N is non-tradable sector

In term of debts, N-sector has much more higher than T-sector. Previous researches find that N-sector in South East Asian countries plays an important role in propagating financial debacle since most of them had revenues in local currencies but their leverages were in foreign currencies. By this research it seems that N-sector has a higher debt ratio as showed by test of variance, which are shown by t-stat in 10.08 and z-stat in 10.375 (both are significant in 1 percent). During the crisis (1997 -1998) firms have a higher rate of debt due to currency depreciation. Data from Bank Indonesia shows that outstanding of investment credit augmented during the crisis period even though interest rates have been increased 17 . It seems that most of firms failed to reschedule their debts and for avoiding the risk of bankruptcy, they had to own to external parties.

Many firms have been collapse. But they were not liquidated rather than were taken over by IBRA (Indonesian Banking Restructuring Agency) which has a main task to restructure banking sector by taking over firms with bad-debts. In table 7 (in appendix), we can see the 200 highest debtors under IBRA were dominated by services and manufacturing companies. It means that both sector (T and N), even they face the same shock, have comparable responses.

However, both sectors have significant different in profitability proxied by lag of sales and debt level. In the total period (1994 -2005) N-sector has much higher level of debts. In term of profitability, N sector has higher than T sector (in t-stat) of profitability but less in median (z-stat) of profitability. It could be happened since N sector has higher standard deviation of profitability (8.883) measured by the lag of sales rather than T sector (4.576).

It can also be argued that N sector has higher heterogeneity of firms concerning firm profitability. Analysis of the sectoral behavior will be described bellow. But it should be important to note that by univariate and descriptive analysis, the behavior of T and N sector is not strikingly different even due to financial crisis.

Multivariate Analysis

Tradable and Non-tradable Sector Analysis

This research uses two famous methods of panel data analysis, namely fixed effect and random effect models. The fixed-coefficient model assumes that each company has different coefficients or that the variation of coefficients is correlated with the explanatory variables, meanwhile random-coefficient model assumes that, conditional on companyspecific effects, the remaining slope coefficients are randomly distributed around a certain mean within each of these groups [START_REF] Hsiao | A panel analysis of liquidity constraints and firm investment[END_REF]. Hausman-test leads us to select the most appropriate estimation. The rejection of the Hausman-test indicates that the fixed effect model is better and therefore it must be chosen 18 .

17 See graph 1, 2, 3, 4 and 5 in appendix 18 In this study, I use 5 percent of significant level as a measurement to reject null hypothesis. The result of the Hausman test is attached together with the result of regression in appendix [START_REF] Fazzari | Financing constraints and corporate investment[END_REF] predict that firms, which have higher coefficient correlation in cash flow and investment, are firms, which have more financially constraints. In this research we can find (table 2 in appendix) that before crisis, the coefficient correlation of T-sector is (-10.038), but it is not significant. In this period, Nsector has -77.186 in 99 percent level of confidence. It means that T-sector has more financial constraint than N sector in the period of before crisis. But in post-crisis, N-sector has much higher coefficient correlation (28.772 in 1 percent of significant level > -22.009 not significant) of the firm-level investment and its liquidity, which lead us to understand that in post-crisis period N-sector was undermined much more severe in their financial constraint.

These findings are consistent with the argument on boom-burst of the sector development as predicted by Tornell and Wastermann (2002). They predict that in the period of boom, N-sector will develop faster rather than T-sector but in the burst period N-sector should be much more difficult to recover.

In this research we also find that Tobin Q is not effective for controlling the relation between investment and liquidity since the coefficient is not significant in all period of time (before and post-crisis, even in total period). Meanwhile, as presented in table 2 (in appendix), debt is very persistent in our estimation, since significant level for all period (total period, before and post-crisis) is relatively high (1 percent). Furthermore, the signs of the coefficient of total debt are always positive. It lead to the explanation that most firms use external financing for their activities. This piece of evidence is in line with trade-off theory predicting that financing choice is irrelevant in investment decision since capital market perfectly work so that firms can access external capital easily and without cost [START_REF] Modigliani | The Cost of Capital, Corporation Finance and the Theory of Investment[END_REF]Miller 1958, 1963).

In our case, coefficient of total debt to investment before crisis period is much higher (1.071) than coefficient in post-crisis period (0.120), both with 1 percent of significant level.

It could indicate that before crisis firms in Indonesia were more exposed to the external debt, but in post-crisis period debts diminished. By the descriptive data mentioned before (graph 4 above), it is especially the case for N-sector.

Robustness check proceeded by employing estimation without lag of sales and Tobin Q support the main finding (table 3 in appendix), which in before crisis period T-sector has higher coefficient correlation with investment (-5.401 > -73.008). It means that liquidity is more important in T-sector or in other words T-sector is more financially constrained.

Meanwhile in post crisis period, N-sector has much higher coefficient (27.354 > -17.868).

And hence, by excluding the expectation of firm profitability, the findings are robust that during boom period N-sector grows faster and in burst period T-sector easily recover.

Industrial Sectors Analysis

The main idea of this section is to capture the effect of industrial sector on the relation between investment and internal liquidity. This paper considers industrial sectors by the definition of JXS, which is divided into 9 sub-sectors 19 . The results of the effect of industrial sector dummies are attached in table 6 in appendix. We run regression by equation ( 4).

It is shown that sector 6 (property, real estate and building construction) has the highest coefficient correlation (101.125) on investment and cash flow. It means that for the case of sector 6, investment does not decrease with their internal liquidity. It seems that sector 6 use external financing to maintain their level of investment. This evidence is consistent with some investigations around financial crisis in South East Asia which give explanation on bubble economy20 .

Meanwhile, in the case of sector 7 (infrastructure, utilities and transportation) the coefficient correlation of investment and liquidity is very weak (-265.076) with high level of confidence (significant in 1 percent). It could be argued that firms in sector 7 use their internal liquidity to maintain their investment level. More investment means less cash flows. By graphical observation it seems that sector 6 has high investment volatility.

Generally, due to the 1997 currency depreciation the investment level of firms in sector 6 diminishes significantly. In the following graph (graph 12), it is important to note that property sector has a high ratio of debt which could be risky. Property sector is basically an unproductive sector.

This descriptive data support the regression result due to the role of property sector (nontradable sector) in risk to exacerbate crisis in Indonesia.

Conclusion

The main finding of this paper is that during boom period, T-sector has greater coefficient correlation on the relation between investment and its internal liquidity.

Oppositely, during burst period, N-sector has much greater on the relation. It means that Tsector is more financially constrained during boom period, but during burst period N-sector has greater financial constraint. These evidences lead us to the understanding that during boom period N-sector increases faster than T-sector, but during burst period, inversely Tsector grows faster.

By employing the innovation of FHP (1988) on the sensitivity of firm-level investment and its internal liquidity, this paper supports the argument that there is an asymmetric financing opportunities between T and N-sector undermining financial fragility, such as described by Tornell and Wastermann (2002). On top of that, by qualitative observation, we can infer that both sector (T and N) react comparably when the financial crisis is present.

However, in term of firm profitability and debts, there are great different responses among firms in T and N-sector. Both variables should be pertinent indicators for financing policies and performance of the firms.

Furthermore, by employing industrial sector dummies, we find that sector 6 in our sample (sector property, real estate & building construction) has a highest positive correlation with investment. In this sector, cash flow increases with investment: the higher investment level is the bigger internal liquidity. It leads us to the understanding that investment is not financed by internal sources, but by external ones. Accordingly, we can predict that sector 6 should be the most leveraged firm in Indonesia. This evidence seems to be consistent with the thesis of Asian bubble as proposed by Krugman (1998) for example, who explains that excessive debt in property sector becomes one of the most important factors propagating Asian crisis.

And hence, this research provides two pieces of evidence that could be important for further the research on the field of firm behavior in Indonesia. First, N-sector develops faster in boom period but it is more difficult to recover when the crisis hit. This finding is showed by the empirical evidence that T sector has higher sensitivity on liquidity and investment in before crisis period (1994 -1996) but N-sector has much higher in post-crisis period (1998 -2004).

Second, generally firms in Indonesia still prefer external debt for financing their investment activities, even in post crisis period. In this case, it supports our understanding that capital structure of the firms in Indonesia is actually prone to financial fragility that could easily lead to the financial crisis.

However, this research has important limitation in line with the data availability, which is poorly constructed. The solution of the lack of several variables of data is covered by manual works. It is the case for data of cash flow variable. This could be important limitation in two senses. First, poor availability of data lead limited analysis, and second, efforts to provide data by manual works could contain serious errors, which are commonly present in the manual works.

Other important problem in this research is the high volatility of data. To deal with this problem, we exclude many companies which contain volatile data in certain variables. And as the consequence, the number of firms examined in this research drop significantly.

For further research, this paper needs improvement on econometrical methods for gaining more rigorous results. The general theme of this paper is important for further studies on investment behavior in Indonesia. The asymmetric financing opportunities between different groups of the firms could be interesting to be investigated for gaining better understanding on firm behavior concerning on the financial fragility and economic vulnerability in Indonesia. In the future research, it should be interesting to develop asymmetric financing opportunities between different group, such as family and non-family firms, bank and non-bank relation, MNC and DC, etc. (1994 -2004) ( 1994 -1996) (1997 -1998) (1999-2004) (1994 -2004) (1994 -1996) ( 1997 -1998) (1999-2004 (1994 -2004) (1994 -1996) (1997 -1998) (1999-2004 (1994 -2004) (1994 -1996) (1997 -1998) (1999 -2004

Intercept

  3. Sector C (services) = property, real estate & building construction (sector 6), infrastructure, utilities & transportation (sector 7), trade, service & investment (sector 9) 15

  's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Note: T is tradable sector, N is non-tradable sector

  Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Sector 1 = agriculture Sector 2 = mining Sector 3 = basic industry & chemical Sector 4 = miscellaneous industry Sector 5 = consumer good industry Sector 6 = property, real estate & building construction Sector 7 = infrastructure, utilities & transportation Sector 9 = trade, service & investment Meanwhile, by observing graph 8 below, it seems that due to currency depreciation firms in sector 2 (mining) decrease significantly their cash flow. Firms in sector 4 (miscellaneous industry), which include industries such as machinery and heavy equipment, automotive and components, cable and electronics, have high level of cash flow. Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Sector 1 = agriculture Sector 2 = mining Sector 3 = basic industry & chemical Sector 4 = miscellaneous industry Sector 5 = consumer good industry Sector 6 = property, real estate & building construction Sector 7 = infrastructure, utilities & transportation Sector 9 = trade, service & investment In graph 9 below, we can see that firms in sector 5 (consumer good) have relatively high market expectation. It seems that firms in sector 5 have high performance in the capital market. property, real estate & building construction Sector 7 = infrastructure, utilities & transportation Sector 9 = trade, service & investment By data descriptive below (graph 10), we find that sector 9 (trade, service and investment) gain most profitable revenue. Currency crisis does not reduce the sales of the firms in sector 9. This evidence can not easily be described. It need further research to clarify this sign. Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Sector 1 = agriculture Sector 2 = mining Sector 3 = basic industry & chemical Sector 4 = miscellaneous industry Sector 5 = consumer good industry Sector 6 = property, real estate & building construction Sector 7 = infrastructure, utilities & transportation Sector 9 = trade, service & investment In term of working capital, in the aftermath of crisis, firms in sector agriculture have least ratio of working capital to capital stock. It means that for supporting their activities in the same level they did not need employing more working capital, since their revenues can cover the needs of their activities. Source: author's calculation based on JSX's database and Indonesian Capital Market Directory provided by ECFIN Sector 1 = agriculture Sector 2 = mining Sector 3 = basic industry & chemical Sector 4 = miscellaneous industry Sector 5 = consumer good industry Sector 6 = property, real estate & building construction Sector 7 = infrastructure, utilities & transportation Sector 9 = trade, service & investment

  property, real estate & building construction Sector 7 = infrastructure, utilities & transportation Sector 9 = trade, service & investment Data from Bank Indonesia as shown in graph 5 in appendix describes how nontradable sector actually have been more exposed to foreign debts. Data of investment credit outstanding of commercial banks in foreign currency shows that before crisis, the flux of foreign debt among tradable and non-tradable sector happened in inverse. Before crisis Tsector has had more foreign debt but in post crisis period N-sector exceeded.

  Azis, "What Would Have Happened in Indonesia if Different Economic Policies Had Been Implemented When the Crisis Started?", Asian Economic Papers, volume 1, Issue 2, Spring 2002. And also Iwan Jaya Azis, "Modelling Crisis Evolution and Counterfactual Policy Simulations: A Country Case Study", ADB Institute Working Paper Series, No.23, August 2001

7

Bank Indonesia has conducted a research on the presence of credit crunch following financial crisis in Indonesia. For further information, the result of study is attached in the www.bi.go.id : "Credit Crunch In Indonesia in the Aftermath of the Crisis: Facts, Causes and Policy Implications" Working Paper, 2000, Directorate of Economic Research and Monetary Policy Bank Indonesia, prepared by team (Agung Juda, Bambang Kusmiarso, Bambang Pramono, Erwin G. Hutapea, Andry Prasmuko, Nugroho Joko Prastowo).

  The soaring of interest rate following sharp depreciation of exchange rate in 1997 has caused the increase of firm debt equity ratio. High interest rate attacks the firms leverage. It means that the hike of interest rate influences directly the corporate value or firm net worth.

Table 2 . Regression of the Sensitivity of T and N sectors

 2

	Total Period	Before Crisis During Crisis	After Crisis

Table 3 . Regression without Tobin Q and Sales

 3

	-2.192 ***	-1.949 ***	0.901 *	-0.266 ***
	(0.163)	(0.753)	(0.463)	(0.059)

Table 4 . Regression without Tobin Q

 4

	)

Table 5 . Regression of T and N sectors (partially)

 5 Fixed Effects and RE is Random Effects. *, **, ***, denote significance at the, 10 percent, 5 percent and 1 percent levels, respectively. Standard deviation is reported in parentheses for specifications

	)

Table 6 . Regression for Industrial sector

 6 Fixed Effect and RE is Random Effect. *, **, ***, denote significance at the, 10 percent, 5 percent and 1 percent levels, respectively. Standard deviation is reported in parentheses for specifications , **, ***, denote significance at the, 10 percent, 5 percent and 1 percent levels, respectively. Standard deviation is reported in parentheses for specifications

	)

*

Important indication of the presence of credit crunch is the declining of the ratio of credit to GDP

This study is inspired by[START_REF] Espanol | Why exportable firms can be financially constrained in a recently liberalized economy? A puzzle based[END_REF] employing the discrimination of tradable and non-tradable sector for analysing the Argentinean firms around financial crisis.

Data taken from East Asia Update: Solid Growth, New Challenges, published by The World Bank, March 2006, page 8. The average real GDP growth around 8 countries (Indonesia, Philippines, Malaysia, Thailand, Hong Kong, Korea, Singapore and Taiwan, China) quoted in this report is 6.6 %, which is higher from real GDP growth in Indonesia (4.9 %).

In 1993, the World Bank considered Indonesia as one of the best performing countries in the world by about 7 % economic growth during several years. Indonesia is one of the East Asian Miracles. See World Bank Policy Research Reports titled "The East Asian Miracle: Economic Growth and Public Policy", Washington DC., 1993.

On August 14 th 1997, the monetary authority in Indonesia decided to adopt a free-floating exchange rate policy since the depreciation of Rupiah was very high. Indonesian Rupiah (IDR) depreciated sharply to United States Dollar (USD); from 4,950 IDR/1 USD in December 1997 to 15,000 IDR/1 USD in June 1998.

Despite of the reluctant of banking sector to offer credit to the firm sector, the survey of Bank Indonesia for 120 firms concerning on the financing behavior following a financial crisis in Indonesia also confirm that firms sector is reluctant to access credit from banking sector.

See Juda Agung et al. (2000).

The comparison with neighbouring countries in East Asia and Latin America is addressed also in the study[START_REF] Juda | Credit crunch in Indonesia in the aftermath of the crisis: facts, causes and policy implications[END_REF]

This issue is still debatable.[START_REF] Espanol | Why exportable firms can be financially constrained in a recently liberalized economy? A puzzle based[END_REF], for example, provides empirical evidence that following a financial crisis in Argentine, tradable sector is more financially constrained than non-tradable sector.

Different from MM, FHP assume that external finance is more expensive since the asymmetric information is presence.

In their seminal paper, Theory of Capital Structure, published in 1958, MM describe that firm financial structure will not affect its market value in frictionless capital market. They assume that information is perfect in capital market.

We exclude financial sector (sector 8) since financial statement of this sector is substantially different from other sector industries

This study use time lag variables for deflator, which is gross capital. Consequently, the time of study lessen one period due to the absence of deflator for last period (1994).

See for example Paul Krugman (1998) "What happened to Asia?" mimeo, MIT

 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003