yuuxu/progressive-permutation-alignment

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}
}