  The intrinsic rotation invariance lies at the core of matching point clouds
with handcrafted descriptors. However, it is widely despised by recent deep
matchers that obtain the rotation invariance extrinsically via data
augmentation. As the finite number of augmented rotations can never span the
continuous SO(3) space, these methods usually show instability when facing
rotations that are rarely seen. To this end, we introduce RoITr, a
Rotation-Invariant Transformer to cope with the pose variations in the point
cloud matching task. We contribute both on the local and global levels.
Starting from the local level, we introduce an attention mechanism embedded
with Point Pair Feature (PPF)-based coordinates to describe the pose-invariant
geometry, upon which a novel attention-based encoder-decoder architecture is
constructed. We further propose a global transformer with rotation-invariant
cross-frame spatial awareness learned by the self-attention mechanism, which
significantly improves the feature distinctiveness and makes the model robust
with respect to the low overlap. Experiments are conducted on both the rigid
and non-rigid public benchmarks, where RoITr outperforms all the
state-of-the-art models by a considerable margin in the low-overlapping
scenarios. Especially when the rotations are enlarged on the challenging
3DLoMatch benchmark, RoITr surpasses the existing methods by at least 13 and 5
percentage points in terms of Inlier Ratio and Registration Recall,
respectively.
