  We present a novel non-iterative learnable method for partial-to-partial 3D
shape registration. The partial alignment task is extremely complex, as it
jointly tries to match between points and identify which points do not appear
in the corresponding shape, causing the solution to be non-unique and ill-posed
in most cases.
  Until now, two principal methodologies have been suggested to solve this
problem: sample a subset of points that are likely to have correspondences or
perform soft alignment between the point clouds and try to avoid a match to an
occluded part. These heuristics work when the partiality is mild or when the
transformation is small but fails for severe occlusions or when outliers are
present. We present a unique approach named Confidence Guided Distance Network
(CGD-net), where we fuse learnable similarity between point embeddings and
spatial distance between point clouds, inducing an optimized solution for the
overlapping points while ignoring parts that only appear in one of the shapes.
The point feature generation is done by a self-supervised architecture that
repels far points to have different embeddings, therefore succeeds to align
partial views of shapes, even with excessive internal symmetries or acute
rotations. We compare our network to recently presented learning-based and
axiomatic methods and report a fundamental boost in performance.
