Point Cloud Processing via Recurrent Set Encoding
This work addresses efficient spatial feature learning for 3D point clouds, which is important for applications like robotics and autonomous driving, but it appears incremental as it builds on existing paradigms with hybrid methods.
The paper tackles 3D point cloud processing by proposing a permutation-invariant network with a recurrent set encoder and convolutional aggregator, achieving competitive results on benchmarks with significant efficiency gains compared to state-of-the-art methods.
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.