  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 a deep neural network that is
invariant to permutations of the input points. This design is what enables our
descriptors to generalise across domains. We evaluate and compare our
descriptors with alternative handcrafted and deep learning-based descriptors on
several indoor and outdoor datasets that are reconstructed by using both RGBD
sensors and laser scanners. Our descriptors outperform most recent descriptors
by a large margin in terms of generalisation, and also become the state of the
art in benchmarks where training and testing are performed in the same domain.
