BachiLi/redner

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1444 stars · 145 forks observed · 2026-08-28

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

redner is a differentiable Monte Carlo ray tracer that computes exact gradients of rendered images with respect to arbitrary scene parameters like geometry, materials, lighting, and camera pose. It integrates with PyTorch and TensorFlow, supports physically-based rendering with correct handling of discontinuities via edge sampling, and offers a fast deferred rendering mode for local shading.

Use cases

• inverse rendering through gradient descent
• compute gradients of rendered images with respect to 3D scene parameters
• optimize scene geometry, materials, and lighting from images
• generate adversarial examples for neural networks
• physically-based rendering with shadows and global illumination gradients
• fast local shading with correct gradient estimation

When to choose

• you need unbiased, non-approximated rendering gradients including discontinuities
• you want physically-based differentiable rendering with shadows and global illumination
• your pipeline uses PyTorch or TensorFlow and needs a differentiable renderer
• you are doing inverse rendering or 3D reconstruction from images

When to avoid

• you need a general-purpose production renderer without gradient requirements
• you need active community support or frequent updates
• you need real-time rendering performance
• you need TensorFlow support on Windows

Facets

library · maturity maintenance

graphics simulation machine-learning computer-vision computer-vision graphics machine-learning deep-learning python windows differentiable-rendering ray-tracing inverse-rendering monte-carlo pytorch tensorflow cuda linux macos gpu

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