Learning general and distinctive 3D local deep descriptors for point cloud registration. (arXiv:2105.10382v3 [cs.CV] UPDATED)

An effective 3D descriptor should be invariant to different geometric
transformations, such as scale and rotation, robust to occlusions and clutter,
and capable of generalising to different application domains. We present a
simple yet effective method to learn general and distinctive 3D local
descriptors that can be used to register point clouds that are captured in
different domains. Point cloud patches are extracted, canonicalised with
respect to their local reference frame, and encoded into scale and
rotation-invariant compact descriptors by …

3d arxiv cloud cv general learning registration