We present a new permutation-invariant network for 3D point cloud processing.
Our network is composed of a recurrent set encoder and a convolutional feature
aggregator. Given an unordered point set, the encoder firstly partitions its
ambient space into parallel beams. Points within each beam are then modeled as
a sequence and encoded into subregional geometric features by a shared
recurrent neural network (RNN). The spatial layout of the beams is regular, and
this allows the beam features to be further fed into an efficient 2D
convolutional neural network (CNN) for hierarchical feature aggregation. Our
network is effective at spatial feature learning, and competes favorably with
the state-of-the-arts (SOTAs) on a number of benchmarks. Meanwhile, it is
significantly more efficient compared to the SOTAs.

本文提出了一种新的置换不变网络用于 3D 点云处理，该网络由循环集编码器和卷积特征聚合器组成，对于无序点集，使用循环神经网络（RNN）对其进行编码并将功能集成到 2D 卷积神经网络（CNN）中，用于层次化特征聚合，该网络在空间特征学习方面表现出色，在多个基准测试中表现优异， 与最新技术水平相比也更加高效。