google-research/multinerf

Health v2 · maintenance only

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3808 stars · 354 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Google Research's official code release for three NeRF papers: Mip-NeRF 360, Ref-NeRF, and RawNeRF, written in JAX. It trains neural radiance field models from images to enable novel view synthesis, reflection handling, and reconstruction from raw photos.

Use cases

• train a NeRF model on my own photos to render novel views
• reproduce Mip-NeRF 360 paper results on unbounded scenes
• render 3D scenes with accurate reflections using Ref-NeRF
• reconstruct scenes from raw camera data with RawNeRF
• compute camera poses from images with COLMAP and train a radiance field
• generate fly-through videos from a trained NeRF checkpoint

When to choose

• you need reference implementations of Mip-NeRF 360, Ref-NeRF, or RawNeRF for research
• you work in JAX and want GPU/TPU-accelerated NeRF training
• you want to reproduce published CVPR 2022 paper metrics exactly

When to avoid

• you need a production-ready or user-friendly 3D capture tool
• you prefer PyTorch-based NeRF frameworks with active community support
• you need real-time rendering or interactive viewer features

Facets

library · maturity maintenance

machine-learning deep-learning graphics simulation computer-vision graphics machine-learning python nerf neural-radiance-fields jax 3d-reconstruction novel-view-synthesis research-code cvpr-2022 research gpu linux

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