EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation. (arXiv:2203.13254v4 [cs.CV] UPDATED)

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is
a long-standing problem in computer vision. Driven by end-to-end deep learning,
recent studies suggest interpreting PnP as a differentiable layer, so that
2D-3D point correspondences can be partly learned by backpropagating the
gradient w.r.t. object pose. Yet, learning the entire set of unrestricted 2D-3D
points from scratch fails to converge with existing approaches, since the
deterministic pose is inherently non-differentiable. In this paper, we propose
the EPro-PnP, a …

arxiv cv perspective pnp