  We present Lepard, a Learning based approach for partial point cloud matching
in rigid and deformable scenes. The key characteristics are the following
techniques that exploit 3D positional knowledge for point cloud matching: 1) An
architecture that disentangles point cloud representation into feature space
and 3D position space. 2) A position encoding method that explicitly reveals 3D
relative distance information through the dot product of vectors. 3) A
repositioning technique that modifies the crosspoint-cloud relative positions.
Ablation studies demonstrate the effectiveness of the above techniques. In
rigid cases, Lepard combined with RANSAC and ICP demonstrates state-of-the-art
registration recall of 93.9% / 71.3% on the 3DMatch / 3DLoMatch. In deformable
cases, Lepard achieves +27.1% / +34.8% higher non-rigid feature matching recall
than the prior art on our newly constructed 4DMatch / 4DLoMatch benchmark.
