README.md

metadata

license: mit
task_categories:
  - feature-extraction
viewer: false
tags:
  - weight-space-learning
  - nerf
  - graph-metanetwork

Weight Space Representation Learning on Diverse NeRF Architectures (ICLR 2026)

This repository contains the dataset for the paper Weight Space Representation Learning on Diverse NeRF Architectures. The framework is capable of processing NeRFs with diverse architectures (MLPs, tri-planes, and hash tables) by training a Graph Meta-Network to obtain architecture-agnostic latent spaces.

Usage

You can use the official scripts provided in the GitHub repository to interact with the data.

Graph computation

To compute the graphs of NeRFs (e.g., test set of the MLP architecture):

python export_graphs.py --data-root ./data --dataset shapenet --arch mlp --split test

Embedding computation

To compute embeddings produced by the trained $\mathcal{L}_{\text{R+C}}$ encoder:

python export_embs.py --ckpt_name l_rec_con --data.root ./data --dataset shapenet --arch mlp --split test

NeRF weights

Main dataset structure:

.
└── nerf
    └── shapenet
        ├── hash
        │   └── class_id
        │       └── nerf_id
        │           ├── train
        │           │   └── *.png              # object views used to train the NeRF
        │           ├── grid.pth               # nerfacc-like occupancy grid parameters
        │           ├── nerf_weights.pth       # nerfacc-like NeRF parameters
        │           └── transforms_train.json  # camera poses
        ├── mlp
        │   └── class_id
        │       └── nerf_id
        │           ├── train
        │           │   └── *.png                 
        │           ├── grid.pth                
        │           ├── nerf_weights.pth        
        │           └── transforms_train.json
        ├── triplane
        │   └── class_id
        │       └── nerf_id
        │           ├── train
        │           │   └── *.png                 
        │           ├── grid.pth                
        │           ├── nerf_weights.pth        
        │           └── transforms_train.json
        ├── test.txt                           # test split
        ├── train.txt                          # training split
        └── val.txt                            # validation split

Unseen architectures (nerf/shapenet/hash_unseen, nerf/shapenet/mlp_unseen, and nerf/shapenet/triplane_unseen) and Objaverse NeRFs (nerf/objaverse) have analogous directory structures.

NeRF graphs

Main dataset structure:

.
└── graph
    └── shapenet
        ├── hash
        │   ├── test
        │   │   └── *.pt  # torch_geometric-like graph data
        │   ├── train
        │   │   └── *.pt
        │   └── val
        │       └── *.pt
        ├── mlp
        │   ├── test
        │   │   └── *.pt
        │   ├── train
        │   │   └── *.pt
        │   └── val
        │       └── *.pt
        └── triplane
            ├── test
            │   └── *.pt
            ├── train
            │   └── *.pt
            └── val
                └── *.pt

Unseen architectures (graph/shapenet/hash_unseen, graph/shapenet/mlp_unseen, and graph/shapenet/triplane_unseen) and Objaverse NeRFs (graph/objaverse) have analogous directory structures.

NeRF embeddings

Main dataset structure:

.
└── emb
    └── model
        └── shapenet
            ├── hash
            │   ├── test
            │   │   └── *.h5
            │   ├── train
            │   │   └── *.h5
            │   └── val
            │       └── *.h5
            ├── mlp
            │   ├── test
            │   │   └── *.h5
            │   ├── train
            │   │   └── *.h5
            │   └── val
            │       └── *.h5
            └── triplane
                ├── test/
                │   └── *.h5
                ├── train
                │   └── *.h5
                └── val
                    └── *.h5

where models are:

• l_con, aka $\mathcal{L}_\text{C}$
• l_rec, aka $\mathcal{L}_\text{R}$
• l_rec_con, aka $\mathcal{L}_\text{R+C}$

Unseen architectures (emb/model/shapenet/hash_unseen, emb/model/shapenet/mlp_unseen, and emb/model/shapenet/triplane_unseen) and Objaverse NeRFs (emb/model/objaverse) have analogous directory structures.

Language data

The language directory contains $\mathcal{L}_\text{R+C}$ embeddings (i.e. those found in emb/l_rec_con/shapenet) paired with textual annotations from the ShapeNeRF-Text dataset. This directory structure allows running the official LLaNA code without any additional preprocessing.

Cite us

If you find our work useful, please cite us:

@inproceedings{ballerini2026weight,
  title = {Weight Space Representation Learning on Diverse {NeRF} Architectures},
  author = {Ballerini, Francesco and Zama Ramirez, Pierluigi and Di Stefano, Luigi and Salti, Samuele},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  year = {2026}
}