Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature
This addresses the challenge of fully end-to-end training in point cloud registration for robotics and computer vision applications, offering a novel solution to a known bottleneck.
The paper tackles the problem of point cloud registration by proposing Q-REG, a method that enables end-to-end training by estimating rigid pose from a single correspondence using surface curvature, setting new state-of-the-art results on 3DMatch, KITTI, and ModelNet benchmarks.
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.