NVlabs/nvdiffrec

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2298 stars · 252 forks observed · 2026-09-23

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

nvdiffrec is NVIDIA's official implementation of a CVPR 2022 oral paper that jointly optimizes triangular 3D meshes, PBR materials, and lighting from multi-view images using differentiable rendering and marching tetrahedra. It is a research codebase built on PyTorch with CUDA extensions for differentiable rasterization.

Use cases

• reconstruct a textured 3D mesh from multi-view photos
• extract PBR materials and lighting from images of an object
• inverse rendering research experiments
• generate triangle meshes with topology optimization from image observations
• compare differentiable isosurfacing techniques like FlexiCubes
• reproduce CVPR 2022 paper results

When to choose

• you need research-grade inverse rendering with mesh, material, and lighting joint optimization
• you want to reproduce or extend the nvdiffrec paper
• you have a CUDA-capable GPU and multi-view image datasets
• you want to experiment with differentiable marching tetrahedra or FlexiCubes

When to avoid

• you need a production-ready photogrammetry tool with a GUI
• you lack a CUDA GPU or cannot build CUDA extensions
• you need a permissively licensed library (it uses the Nvidia Source Code License)
• you only need simple 3D scanning without material/lighting estimation

Facets

library · maturity stable

machine-learning deep-learning image-processing graphics simulation computer-vision graphics machine-learning deep-learning python windows cross-platform 3d-reconstruction inverse-rendering differentiable-rendering pytorch mesh-extraction pbr-materials research-code nvidia gpu linux

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markdown · JSON · MCP: product_card(name="NVlabs/nvdiffrec")