yashbhalgat/HashNeRF-pytorch

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1037 stars · 108 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A pure PyTorch implementation of NVIDIA's Instant-NGP multi-resolution hash encoding for training NeRFs, built on top of NeRF-pytorch. It enables researchers to experiment with and extend the hash-encoding method without the original C++/CUDA codebase.

Use cases

• train a NeRF model quickly in pure PyTorch
• experiment with multi-resolution hash encoding for neural graphics primitives
• reconstruct 3D scenes from images with NeRF
• research faster NeRF training methods
• train NeRFs on ScanNet scenes
• render novel views of synthetic objects like chairs and ficus

When to choose

• you want a readable, hackable PyTorch codebase to modify or extend Instant-NGP's hash encoding
• you need NeRF training that runs on modest consumer GPUs
• you want to prototype NeRF research ideas without touching CUDA

When to avoid

• you need maximum training speed or production-grade performance - use NVIDIA's original instant-ngp with tiny-cuda-nn
• you need voxel pruning or accelerated ray tracing, which are listed as TODO
• you need a maintained production library rather than a research codebase

Facets

library · maturity maintenance

machine-learning graphics simulation computer-vision machine-learning graphics artificial-intelligence python nerf instant-ngp hash-encoding pytorch 3d-reconstruction neural-rendering research gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="yashbhalgat/HashNeRF-pytorch")