Ground-Aware Point Cloud Semantic Segmentation for Autonomous Driving

Abstract

Semantic understanding of 3D scenes is essential for autonomous driving. Although a number of efforts have been devoted to semantic segmentation of dense point clouds, the great sparsity of 3D LiDAR data poses significant challenges in autonomous driving. In this paper, we work on the semantic segmentation problem of extremely sparse LiDAR point clouds with specific consideration of the ground as reference. In particular, we propose a ground-aware framework that well solves the ambiguity caused by data sparsity. We employ a multi-section plane fitting approach to roughly extract ground points to assist segmentation of objects on the ground. Based on the roughly extracted ground points, our approach implicitly integrates the ground information in a weakly-supervised manner and utilizes ground-aware features with a new ground-aware attention module. The proposed ground-aware attention module captures long-range dependence between ground and objects, which significantly facilitates the segmentation of small objects that only consist of a few points in extremely sparse point clouds. Extensive experiments on two large-scale LiDAR point cloud datasets for autonomous driving demonstrate that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively.