  Compared to 2D images, 3D point clouds are much more sensitive to rotations.
We expect the point features describing certain patterns to keep invariant to
the rotation transformation. There are many recent SOTA works dedicated to
rotation-invariant learning for 3D point clouds. However, current
rotation-invariant methods lack generalizability on the point clouds in the
open scenes due to the reliance on the global distribution, \ie the global
scene and backgrounds. Considering that the output activation is a function of
the pattern and its orientation, we need to eliminate the effect of the
orientation.In this paper, inspired by the idea that the network weights can be
considered a set of points distributed in the same 3D space as the input
points, we propose Weight-Feature Alignment (WFA) to construct a local
Invariant Reference Frame (IRF) via aligning the features with the principal
axes of the network weights. Our WFA algorithm provides a general solution for
the point clouds of all scenes. WFA ensures the model achieves the target that
the response activity is a necessary and sufficient condition of the pattern
matching degree. Practically, we perform experiments on the point clouds of
both single objects and open large-range scenes. The results suggest that our
method almost bridges the gap between rotation invariance learning and normal
methods.
