State-of-the-art methods for large-scale driving-scene LiDAR semantic
segmentation often project and process the point clouds in the 2D space. The
projection methods includes spherical projection, bird-eye view projection,
etc. Although this process makes the point cloud suitable for the 2D CNN-based
networks, it inevitably alters and abandons the 3D topology and geometric
relations. A straightforward solution to tackle the issue of 3D-to-2D
projection is to keep the 3D representation and process the points in the 3D
space. In this work, we first perform an in-depth analysis for different
representations and backbones in 2D and 3D spaces, and reveal the effectiveness
of 3D representations and networks on LiDAR segmentation. Then, we develop a 3D
cylinder partition and a 3D cylinder convolution based framework, termed as
Cylinder3D, which exploits the 3D topology relations and structures of
driving-scene point clouds. Moreover, a dimension-decomposition based context
modeling module is introduced to explore the high-rank context information in
point clouds in a progressive manner. We evaluate the proposed model on a
large-scale driving-scene dataset, i.e. SematicKITTI. Our method achieves
state-of-the-art performance and outperforms existing methods by 6% in terms of
mIoU.

本文提出了一种基于 Cylinder3D 的 3D 点云语义分割方法，利用 3D 拓扑和结构信息，采用 3D 表征和网络模型，并引入基于尺度分解的上下文建模模块，实现对汽车驾驶场景的 LiDAR 数据进行高效准确的分割。