  In this paper we propose a rotation-invariant deep network for point clouds
analysis. Point-based deep networks are commonly designed to recognize roughly
aligned 3D shapes based on point coordinates, but suffer from performance drops
with shape rotations. Some geometric features, e.g., distances and angles of
points as inputs of network, are rotation-invariant but lose positional
information of points. In this work, we propose a novel deep network for point
clouds by incorporating positional information of points as inputs while
yielding rotation-invariance. The network is hierarchical and relies on two
modules: a positional feature embedding block and a relational feature
embedding block. Both modules and the whole network are proven to be
rotation-invariant when processing point clouds as input. Experiments show
state-of-the-art classification and segmentation performances on benchmark
datasets, and ablation studies demonstrate effectiveness of the network design.
