---
license: mit
---

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

[[arXiv](https://arxiv.org/abs/2502.09623) | [project page](https://cvlab-unibo.github.io/gmnerf/)]

Main dataset structure:
```
.
└── nerf/
    └── shapenet/
        ├── hash/                             # compressed into _hash.tar
        │   └── class_id/
        │       └── nerf_id/
        │           ├── train/
        │           │   └── *.png              # object views used to train the NeRF
        │           ├── grid.pth               # occupancy grid parameters
        │           ├── nerf_weights.pth       # NeRF parameters
        │           └── transforms_train.json  # camera poses
        ├── mlp/                               # compressed into _mlp.tar
        │   └── class_id/
        │       └── nerf_id/
        │           ├── train/
        │           │   └── *.png                 
        │           ├── grid.pth                
        │           ├── nerf_weights.pth        
        │           └── transforms_train.json
        ├── triplane/                          # compressed into _triplane.tar
        │   └── 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
```

The official code repository will be [available soon](https://github.com/CVLAB-Unibo/gmnerf). In the meantime, here are some links to the (unpolished) code used to train the NeRFs contained in the dataset:

* [MLP-based NeRF](https://github.com/frallebini/nerf-training/blob/42e1749f5743789003da53aa6f567562497baf39/radiance_fields/nerf_nerf2vec.py#L65)
* [Tri-planar NeRF](https://github.com/frallebini/nerf-training/blob/42e1749f5743789003da53aa6f567562497baf39/radiance_fields/nerf_triplane.py#L20)
* [Hash-based NeRF](https://github.com/frallebini/nerf-training/blob/42e1749f5743789003da53aa6f567562497baf39/radiance_fields/nerf_hash_single_mlp_separate_enc.py#L22)

If you are interested in running this code, follow [this README](https://github.com/CVLAB-Unibo/nf2vec/blob/main/README.md) to install the required libraries.

If you find our work useful, please cite us:

```bibtex
@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}
```