frallebini/gmnerf

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

This repository contains the datasets for the paper Weight Space Representation Learning on Diverse NeRF Architectures. The paper proposes a framework that is capable of processing NeRFs with diverse architectures (MLPs, tri-planes, and hash tables) by training a graph metanetwork to obtain an architecture-agnostic latent space.

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

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 ${\mathcal{L}}_{\text{R+C}}\backslash mathcal\{L\}\_\backslash text\{R+C\}$ 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}
}