license: apache-2.0
library_name: nerfstudio
tags:
- inverse-rendering
- neural-fields
- illumination
- hdr
- nerfstudio
RENI and RENI++ Checkpoints
This repository contains the released checkpoints for RENI++ and the RENI experiments in Learning Representations for Incomplete Spherical Scene Signals.
The release is deliberately divided into groups:
minimal: the headline decoder as a self-contained, PyTorch-only reference artifact and locked CPU rendering example.core: the thesis headline model, with joint Gram-Schmidt invariant conditioning, two HDR brackets and two latent-reset cycles.neusky-prior: the channelwise two-bracket prior used by every released NeuSky checkpoint. This is intentionally not the joint-frame thesis model.thesis: the size, equivariance, invariant-function, seed and latent-reset checkpoints needed for the thesis experiments.published: the final checkpoints from the published RENI and RENI++ experiments, including SH/SG and inverse-rendering baselines.
MODEL_MANIFEST.json records the source run, selected checkpoint, byte size
and SHA256 of every released file. Only final, named checkpoints are included;
optimizer trajectories, failed runs, logs and Weights & Biases state are not.
Download
From a clone of ns_reni, download the headline model:
python scripts/download_models.py model-storage/reni
Download another release group:
python scripts/download_models.py model-storage/reni --group minimal
python scripts/download_models.py model-storage/reni --group neusky-prior
python scripts/download_models.py model-storage/reni --group thesis
python scripts/download_models.py model-storage/reni --group published
List exact model identifiers or download one model:
python scripts/download_models.py --list
python scripts/download_models.py model-storage/reni \
--model thesis-vnjoint-ortho-so2-d100
Files can also be retrieved directly with curl or the Hugging Face CLI.
For example:
hf download jadgardner/reni-models \
--revision v1.1 \
--include "minimal/*" \
--local-dir model-storage/reni
Minimal PyTorch Inference
The minimal group is the quickest way to evaluate the thesis model. It
contains only the 3.57 MiB decoder weights and the code needed to sample an
environment map:
python scripts/download_models.py model-storage/reni --group minimal
cd model-storage/reni/minimal
uv run render.py --weights decoder.pt --output-dir render
It requires no Nerfstudio, tiny-cuda-nn, COLMAP, CUDA, or ns_reni
installation. The artifact includes the joint Vector Neuron frame, attention
decoder, architecture metadata and two-bracket HDR constants. The full
checkpoint remains available in core for continued training and analysis of
the learned training latents.
Data
The corresponding HDR panorama data are released separately as RENI HDR v1.0.
Licence
The checkpoint release follows the Apache 2.0 licence in the RENI++ repository. Third-party source assets and datasets retain their own licences.