# kwea123/nerf_pl

NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning

Repository: https://github.com/kwea123/nerf_pl
Canonical: https://ross.abutalabs.com/products/nerf_pl
Homepage: https://www.youtube.com/playlist?list=PLDV2CyUo4q-K02pNEyDr7DYpTQuka3mbV
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: nerf, neural-radiance-fields, view-synthesis, pytorch, pytorch-lightning, mesh, 3d-reconstruction, volume-rendering, mixed-reality, unity3d, colab, ray-tracing, nerf-in-the-wild, nerf-w
Last push: 2023-08-03T15:02:39+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2334, "days_push": 1126, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2805, forks 458 (observed 2026-08-28T04:07:22.788691+00:00)

## What it is
An unofficial PyTorch implementation of NeRF (Neural Radiance Fields) and NeRF in the Wild built on pytorch-lightning, supporting multi-GPU training, colored mesh reconstruction, and real-time volume rendering in Unity. It prioritizes simpler, faster training and readable code over strict paper reproducibility.

## Use cases
- train a nerf model from photos
- reconstruct 3d mesh from images
- novel view synthesis of a scene
- render nerf scenes in unity
- run nerf training on colab
- understand how nerf works from code
- create mixed reality scenes from radiance fields

## When to choose
- you want a readable, well-commented NeRF implementation for learning
- you need multi-GPU training or Colab notebooks for easy experimentation
- you want to export meshes or render scenes in Unity

## When to avoid
- you need fast, high-quality NeRF training - use instant-ngp or ngp_pl instead
- you need strict paper reproducibility of the official NeRF results
- you need active support for dynamic scenes - see nsff_pl

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, graphics, simulation
- domain: computer-vision, graphics, deep-learning, machine-learning
- platform: python, cross-platform
- tags: nerf, neural-radiance-fields, view-synthesis, volume-rendering, pytorch-lightning, 3d-reconstruction, mesh-reconstruction, unity-integration, colab-notebooks, nerf-in-the-wild, gpu, linux

## Member repositories
- kwea123/nerf_pl (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.788691+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 2026-08-30T08:14:29.379850+00:00, confidence not recorded.
  - readme: https://github.com/kwea123/nerf_pl (fetched 2026-08-28T04:07:22.788691+00:00, sha 807ff0253d6c)
  - homepage: https://www.youtube.com/playlist?list=PLDV2CyUo4q-K02pNEyDr7DYpTQuka3mbV (fetched 2026-08-29T09:54:26.544328+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.