# sxyu/svox2

Plenoxels: Radiance Fields without Neural Networks

Repository: https://github.com/sxyu/svox2
Canonical: https://ross.abutalabs.com/products/svox2
Language: Python
License: BSD-2-Clause
License Family: permissive
Last push: 2023-06-29T15:58:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1788, "days_push": 1161, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2889, forks 354 (observed 2026-08-28T04:07:28.308201+00:00)

## What it is
Official PyTorch/CUDA implementation of Plenoxels, a method for optimizing sparse voxel radiance fields for novel view synthesis without neural networks. It supports NeRF-Blender, LLFF, NSVF, and CO3D dataset formats and renders photorealistic 3D scenes from posed images.

## Use cases
- reconstruct 3d scenes from photos
- novel view synthesis without neural networks
- train radiance fields faster than nerf
- render free viewpoint videos from images
- generate radiance field datasets for research

## When to choose
- you want NeRF-quality novel view synthesis with faster optimization and no MLP
- you have a Linux machine with a CUDA GPU and posed multi-view images
- you need a research baseline or building block for radiance field experiments

## When to avoid
- you are on Windows, which is not officially supported
- you need real-time rendering or instant-ngp-style hash-grid speed
- you want a maintained production library rather than a research codebase

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, graphics, image-processing, gpu-computing
- domain: computer-vision, graphics, machine-learning
- platform: python
- tags: radiance-fields, nerf, voxel-grids, 3d-reconstruction, novel-view-synthesis, cuda-extension, cvpr, research, linux, gpu, cuda

## Member repositories
- sxyu/svox2 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.308201+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:35:21.638689+00:00, confidence not recorded.
  - readme: https://github.com/sxyu/svox2 (fetched 2026-08-28T04:07:28.308201+00:00, sha 8ba04edd5924)
- Data as of 2026-08-30T08:39:29.467469+00:00.