ShichenLiu/SoftRas

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1298 stars · 159 forks observed · 2026-08-28

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

SoftRas is a PyTorch-based differentiable renderer that treats rasterization as a differentiable aggregating process over mesh triangles, enabling gradient back-propagation from images to mesh vertices and attributes. It supports rendering colorized meshes and training 3D reconstruction models from images without 3D supervision.

Use cases

• render 3D meshes differentiably in PyTorch
• reconstruct 3D meshes from single RGB images without 3D supervision
• backpropagate image-based losses to mesh vertices and textures
• train single-view mesh reconstruction networks on multi-view image datasets
• generate soft, blurred, or transparent renderings of meshes for research

When to choose

• you need gradients flowing from rendered images back to 3D mesh geometry or texture
• you are doing image-based 3D reconstruction or inverse graphics research
• you want a differentiable alternative to standard rasterizers in a PyTorch pipeline

When to avoid

• you need a fast production renderer without gradient requirements
• you need support for modern PyTorch versions or recent CUDA toolkits out of the box
• you need features like ray tracing, global illumination, or complex material models

Facets

library · maturity maintenance

graphics machine-learning image-processing simulation computer-vision graphics deep-learning machine-learning python differentiable-rendering pytorch 3d-reconstruction rasterizer cuda iccv-2019 mesh-rendering gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ShichenLiu/SoftRas")