metadata
license: mit
tags:
- nerf
- neural-fields
- permutation-symmetry
- model-merging
Trained NeRF pairs for "Progressive Permutation Alignment with Temperature-Controlled Spectral Sharpening" (ACCV 2026)
Code: https://github.com/yuzu0308/progressive-permutation-alignment
16 vanilla NeRFs (nerfstudio 1.1.5 vanilla-nerf, 1M iterations) trained on the 8 scenes of NeRF Synthetic
with two random seeds each. These are the model pairs used for the interpolation (linear mode connectivity)
experiments of the paper.
| File | Content |
|---|---|
<scene>/model_a.pth |
state dict of the model trained with seed 42 (only the field_coarse.* / field_fine.* entries) |
<scene>/model_b.pth |
same for seed 100 |
<scene>/ref_perms_cpu.pt |
permutations found by the original research code (pa: Ours w/o Ref., pa_wm: Ours), used by the regression test |
Scenes: lego, chair, ficus, hotdog, mic, ship, materials, drums.
pip install huggingface_hub
python scripts/download_weights.py # from the code repository
from ppa.nerf import load_state_dict
a = load_state_dict("weights/lego/model_a.pth")
The LPIPS network weights that nerfstudio stores in its checkpoints are not included; they are not needed for alignment or rendering.
Citation
@inproceedings{horioka2026progressive,
title = {Progressive Permutation Alignment with Temperature-Controlled Spectral Sharpening},
author = {Horioka, Yuta and Chiba, Naoya and Uranishi, Yuki},
booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
year = {2026}
}