We present a novel rotation invariant architecture operating directly on
point cloud data. We demonstrate how rotation invariance can be injected into a
recently proposed point-based PCNN architecture, at all layers of the network,
achieving invariance to both global shape transformations, and to local
rotations on the level of patches or parts, useful when dealing with non-rigid
objects. We achieve this by employing a spherical harmonics based kernel at
different layers of the network, which is guaranteed to be invariant to rigid
motions. We also introduce a more efficient pooling operation for PCNN using
space-partitioning data-structures. This results in a flexible, simple and
efficient architecture that achieves accurate results on challenging shape
analysis tasks including classification and segmentation, without requiring
data-augmentation, typically employed by non-invariant approaches.

本研究提出了一种新的旋转不变结构，可用于操作点云数据以及在神经网络的各个层级中注入了旋转不变性。采用基于球谐函数的核心技术，实现对非刚体物体的局部旋转与整体转型的旋转不变性，并采用空间分区结构进行更高效的汇聚操作，进而最终，在不需要采用数据增广的前提下，成功地通过分类和分割任务对具有挑战性的形状数据进行了高精度的处理。