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In this work, we present a novel method called WSDesc to learn 3D localdescriptors in a weakly supervised manner for robust point cloud registration.Our work builds upon recent 3D CNN-based descriptor extractors, which leveragea voxel-based representation to parameterize local geometry of 3D points.Instead of using a predefined fixed-size local support in voxelization, wepropose to learn the optimal support in a data-driven manner. To this end, wedesign a novel differentiable voxelization layer that can back-propagate thegradient to the support size optimization. To train the extracted descriptors,we propose a novel registration loss based on the deviation from rigidity of 3Dtransformations, and the loss is weakly supervised by the prior knowledge thatthe input point clouds have partial overlap, without requiring ground-truthalignment information. Through extensive experiments, we show that our learneddescriptors yield superior performance on existing geometric registrationbenchmarks.

WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration