BPnP: Further Empowering End-to-End Learning with Back-Propagatable Geometric Optimization
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In this paper we present BPnP, a novel method to do back-propagation through a PnP solver. We show that the gradients of such geometric optimization process can be computed using the Implicit Function Theorem as if it is differentiable. Furthermore, we develop a residual-conformity trick to make end-to-end pose regression using BPnP smooth and stable. We also propose a "march in formation" algorithm which successfully uses BPnP for keypoint regression. Our invention opens a door to vast possibilities. The ability to incorporate geometric optimization in end-to-end learning will greatly boost its power and promote innovations in various computer vision tasks.
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