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---
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}
```
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