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