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reni-models

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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:

bash

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:

bash

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:

bash

hf download jadgardner/reni-models \
  --revision v1.2 \
  --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:

bash

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.

Release v1.2 updates the locked minimal environment to PyTorch 2.13.0 CPU, Pillow 12.3.0 and setuptools 83.0.0. The decoder weights and all full checkpoints are unchanged from v1.1.

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.