| --- |
| 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++](https://github.com/JADGardner/ns_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: |
|
|
| ```bash |
| 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 v1.1 \ |
| --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: |
|
|
| ```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. |
|
|
| ## Data |
|
|
| The corresponding HDR panorama data are released separately as |
| [RENI HDR v1.0](https://huggingface.co/datasets/jadgardner/reni-hdr). |
|
|
| ## Licence |
|
|
| The checkpoint release follows the Apache 2.0 licence in the RENI++ |
| repository. Third-party source assets and datasets retain their own licences. |
|
|
