ashawkey/torch-ngp

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2208 stars · 286 forks observed · 2026-08-28

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

A PyTorch implementation of instant-ngp's NeRF and SDF components with CUDA extensions and an interactive GUI for training and visualization. It also includes implementations of TensoRF, CCNeRF, and D-NeRF adapted to the instant-ngp framework.

Use cases

• train a NeRF model from images in PyTorch
• reconstruct 3D scenes with instant-ngp hash encoding
• visualize NeRF training interactively with a GUI
• implement signed distance fields with neural networks
• model dynamic scenes with D-NeRF
• experiment with TensoRF tensorial radiance fields
• generate novel views of real scenes from photos

When to choose

• you want a readable PyTorch codebase for NeRF research instead of the original CUDA instant-ngp
• you need an interactive GUI for training and rendering radiance fields
• you want to experiment with NeRF variants like D-NeRF, TensoRF, or composable NeRF in one framework

When to avoid

• you need maximum training/rendering performance of the original NVIDIA instant-ngp implementation
• you want a production-ready, actively maintained NeRF pipeline - the author recommends nerf_template instead
• you need a pure PyTorch solution without CUDA extension compilation

Facets

library · maturity maintenance

machine-learning deep-learning graphics gui gpu-computing computer-vision graphics machine-learning deep-learning python windows nerf instant-ngp sdf neural-radiance-fields pytorch cuda-extension tensorf d-nerf 3d-reconstruction real-time-rendering linux gpu

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