minimal/README.md

Minimal RENI++ Inference

This directory evaluates the thesis RENI++ illumination prior with a small CPU environment containing PyTorch, NumPy and Pillow. It does not install Nerfstudio, tiny-cuda-nn, COLMAP, or the full ns_reni package.

Download the minimal release group from the repository root:

python scripts/download_models.py model-storage/reni --group minimal

Then render a deterministic random sample on CPU:

cd model-storage/reni/minimal
uv run render.py --weights decoder.pt --output-dir render

The release directory is self-contained: it includes the decoder artifact, reference implementation, rendering example, Apache 2.0 licence and locked CPU environment. From the source-tree copy of this example, pass the downloaded model-storage/reni/minimal/decoder.pt path explicitly.

The command writes:

• environment_linear_hdr.pt, a [H, 2H, 3] linear-HDR tensor;
• environment_preview.png, a display-tonemapped preview;
• latent.pt, the [100, 3] latent used for the render.

The output is in the fixed luminance gauge used to train the two-bracket model. Apply a scalar exposure in linear HDR when absolute scene exposure is required.

reni_decoder.py is a release-specific reference implementation. It includes the learned joint Vector Neuron frame, its Gram-Schmidt orthonormalisation, the directional encoding, attention decoder, and two-bracket HDR reconstruction. The full Nerfstudio checkpoint remains the source for continued training and for reproducing analyses of the learned training latents.