  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.
