Scene-Adaptive 3D Semantic Segmentation Based on Multi-Level Boundary-Semantic-Enhancement for Intelligent Vehicles

Abstract

3D semantic segmentation is a key technology of scene understanding in the self-driving field, which remains challenging problems. Recent 3D segmentation methods have achieved competitive results in indoor or typical urban traffic scenes. However, in complex and changeable scenarios where structured features are sparse and irregular, few of these methods could achieve well segmentation results, especially causing blurry and inaccurate boundary distinctions between inter-class objects, drivable areas, and backgrounds. In order to fully harvest boundary information and accurately distinguish the category of points on road and object boundaries in real-time, we present an efficient multi-level boundary-semantic-enhanced model for LiDAR semantic segmentation, which comprehensively discover boundary features in three aspects: first, boundary channels are extracted directly from LiDAR range images as the inputs of boundary-branch; second, the boundary attention module is designed to deeply fuse boundary information into the main segmentation branch; third, a modified discriminator is utilized to raise the perception of boundary information by minimizing the gap between the predicted and true boundaries. Besides, we add a semantic-enhanced module using the similar discriminator to optimize semantic segmentation results in the output end. Quantitative and qualitative evaluations are performed on both structured and unstructured real-world datasets including urban dataset SemanticKITTI, off-road dataset Rellis3D and our unstructured test set. The experimental results validate the effectiveness of the proposed methodology in improving efficiency, accuracy and scene-adaptivity.