  We present a novel and flexible architecture for point cloud segmentation
with dual-representation iterative learning. In point cloud processing,
different representations have their own pros and cons. Thus, finding suitable
ways to represent point cloud data structure while keeping its own internal
physical property such as permutation and scale-invariant is a fundamental
problem. Therefore, we propose our work, DRINet, which serves as the basic
network structure for dual-representation learning with great flexibility at
feature transferring and less computation cost, especially for large-scale
point clouds. DRINet mainly consists of two modules called Sparse Point-Voxel
Feature Extraction and Sparse Voxel-Point Feature Extraction. By utilizing
these two modules iteratively, features can be propagated between two different
representations. We further propose a novel multi-scale pooling layer for
pointwise locality learning to improve context information propagation. Our
network achieves state-of-the-art results for point cloud classification and
segmentation tasks on several datasets while maintaining high runtime
efficiency. For large-scale outdoor scenarios, our method outperforms
state-of-the-art methods with a real-time inference speed of 62ms per frame.
