  Finding correspondences between images or 3D scans is at the heart of many
computer vision and image retrieval applications and is often enabled by
matching local keypoint descriptors. Various learning approaches have been
applied in the past to different stages of the matching pipeline, considering
detector, descriptor, or metric learning objectives. These objectives were
typically addressed separately and most previous work has focused on image
data. This paper proposes an end-to-end learning framework for keypoint
detection and its representation (descriptor) for 3D depth maps or 3D scans,
where the two can be jointly optimized towards task-specific objectives without
a need for separate annotations. We employ a Siamese architecture augmented by
a sampling layer and a novel score loss function which in turn affects the
selection of region proposals. The positive and negative examples are obtained
automatically by sampling corresponding region proposals based on their
consistency with known 3D pose labels. Matching experiments with depth data on
multiple benchmark datasets demonstrate the efficacy of the proposed approach,
showing significant improvements over state-of-the-art methods.
