Nerfacc
v0.5.3Efficient volumetric rendering acceleration library for NeRF with occupancy grids and proposal networks
Development Activity
Sample Renders
Overview
Best for
NeRF researchers who need to accelerate volumetric rendering in custom pipelines without reimplementing occupancy grids or proposal networks from scratch
Not ideal for
Gaussian Splatting workflows, standalone rendering applications, or users without NVIDIA GPU hardware
Strengths
- Provides a unified, high-performance API for the two dominant NeRF acceleration strategies — occupancy grids and proposal networks — eliminating the need to reimplement these techniques from scratch
- Delivers 5-20x speedup for NeRF training and rendering by efficiently skipping empty space and concentrating samples in occupied regions
- Clean, well-documented Python API that integrates naturally with PyTorch training loops — minimal code changes required to accelerate an existing NeRF pipeline
- MIT license enables unrestricted use in both academic research and commercial applications without any licensing concerns
- Proven reliability as the acceleration backbone for Nerfstudio's NeRF methods, validated across thousands of research experiments
Limitations
- Specialized for NeRF-style volumetric rendering only — does not accelerate Gaussian Splatting or other non-volumetric neural rendering methods
- Requires NVIDIA CUDA GPU — no CPU, AMD, Intel, or Apple Silicon support
- Not a standalone renderer — it is a library that must be integrated into an existing NeRF training pipeline with custom code
- Development has slowed as the neural rendering field shifts toward Gaussian Splatting, which uses fundamentally different rendering techniques
- Limited to the occupancy grid and proposal network strategies it implements — novel acceleration approaches require modifying the CUDA kernels directly
Background
Nerfacc is a specialized acceleration library for NeRF-style volumetric rendering, developed as part of the Nerfstudio ecosystem by Ruilong Li at UC Berkeley. It provides high-performance CUDA implementations of the two dominant acceleration strategies for neural radiance fields: occupancy grids (which maintain a 3D grid of binary occupancy to skip empty space) and proposal networks (which learn to allocate ray samples where they matter most, following the Mip-NeRF 360 approach).
The library's value proposition is clear: rather than reimplementing these well-understood acceleration techniques from scratch for each new NeRF project, researchers can integrate nerfacc's optimized kernels into their training pipeline with minimal code changes. The result is a 5-20x speedup in both training and rendering, achieved by concentrating computation on regions of the scene that actually contain content. The Python API is designed to integrate naturally with PyTorch training loops.
Nerfacc is used as the acceleration backbone for several of Nerfstudio's NeRF methods and has been adopted by numerous independent research projects. However, as the neural rendering field has increasingly shifted toward Gaussian Splatting (which uses rasterization rather than volume rendering), nerfacc's development has slowed. It remains the best option for researchers working with NeRF-style volumetric representations who need optimized sampling and rendering acceleration.
Quick Start
pip install nerfaccRelated Renderers
Community & Resources
Community
Paper & Citations
Tutorials & Resources
Performance Benchmarks
No benchmark data available for Nerfacc yet.
Benchmarks will be added as more renderers are tested across our standard scene suite.
Learn about our methodology