tjiiv-cprg/EPro-PnP

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1175 stars · 110 forks observed · 2026-08-28

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

EPro-PnP is a probabilistic Perspective-n-Points (PnP) layer for end-to-end 6DoF monocular object pose estimation networks, built on PyTorch. It treats PnP as a differentiable probabilistic optimization layer, generalizable to other nested argmin learning models.

Use cases

• estimate 6DoF object pose from a single image
• train end-to-end pose estimation networks with a differentiable PnP layer
• solve perspective-n-point with probabilistic weighting of 2D-3D correspondences
• perform monocular 3D object detection
• backpropagate through geometric optimization layers in PyTorch

When to choose

• you need differentiable PnP inside a deep learning pipeline
• you are doing monocular 6DoF pose estimation or 3D object detection research
• you want a probabilistic alternative to categorical softmax for argmin optimization layers

When to avoid

• you need a fast non-differentiable PnP solver for classical pipelines
• you lack a GPU or PyTorch environment
• you need production-ready real-time inference rather than research code

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning autonomous-vehicles python pose-estimation 6dof perspective-n-point pnp monocular 3d-object-detection pytorch differentiable-optimization cvpr-2022 levenberg-marquardt linux gpu

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