  Successful point cloud registration relies on accurate correspondences
established upon powerful descriptors. However, existing neural descriptors
either leverage a rotation-variant backbone whose performance declines under
large rotations, or encode local geometry that is less distinctive. To address
this issue, we introduce RIGA to learn descriptors that are Rotation-Invariant
by design and Globally-Aware. From the Point Pair Features (PPFs) of sparse
local regions, rotation-invariant local geometry is encoded into geometric
descriptors. Global awareness of 3D structures and geometric context is
subsequently incorporated, both in a rotation-invariant fashion. More
specifically, 3D structures of the whole frame are first represented by our
global PPF signatures, from which structural descriptors are learned to help
geometric descriptors sense the 3D world beyond local regions. Geometric
context from the whole scene is then globally aggregated into descriptors.
Finally, the description of sparse regions is interpolated to dense point
descriptors, from which correspondences are extracted for registration. To
validate our approach, we conduct extensive experiments on both object- and
scene-level data. With large rotations, RIGA surpasses the state-of-the-art
methods by a margin of 8\degree in terms of the Relative Rotation Error on
ModelNet40 and improves the Feature Matching Recall by at least 5 percentage
points on 3DLoMatch.
