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arXiv · 2309.16023

Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature

Shengze Jin·Daniel Barath·Marc Pollefeys·Iro Armeni
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Abstract

Point cloud registration has seen recent success with several learning-based methods that focus on correspondence matching and, as such, optimize only for this objective. Following the learning step of correspondence matching, they evaluate the estimated rigid transformation with a RANSAC-like framework. While it is an indispensable component of these methods, it prevents a fully end-to-end training, leaving the objective to minimize the pose error nonserved. We present a novel solution, Q-REG, which utilizes rich geometric information to estimate the rigid pose from a single correspondence. Q-REG allows to formalize the robust estimation as an exhaustive search, hence enabling end-to-end training that optimizes over both objectives of correspondence matching and rigid pose estimation. We demonstrate in the experiments that Q-REG is agnostic to the correspondence matching method and provides consistent improvement both when used only in inference and in end-to-end training. It sets a new state-of-the-art on the 3DMatch, KITTI, and ModelNet benchmarks.

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Shengze Jin, Daniel Barath, Marc Pollefeys, Iro Armeni. 2023-09-27. Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature. https://arxiv.org/abs/2309.16023

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