PDES-Net: LiDAR point cloud semantic segmentation network based on point-wise distance encoding and pointed-seg head

Abstract

Light Detection and Ranging (LiDAR)-based semantic segmentation is significant in advanced autonomous driving systems. However, it is challenging to achieve accurate and efficient semantic segmentation because of the sparse and uneven distribution of LiDAR point cloud data. To address the above problem, we propose a LiDAR point cloud semantic segmentation network based on point-wise distance encoding and pointed-seg head, named as PDES-Net. The point-wise distance encoding mechanism introduces normalized continuous depth information as a supplementary feature into the network, enhances the perception of both near and distant points, and reduce geometric information loss. The pointed-seg head module adaptively integrates multilevel features through learnable weight coefficients, and enhances the expressive ability of point-wise prediction. The performance of the proposed PDES-Net is evaluated on the publicly available benchmarks, SemanticKITTI and nuScenes, achieving mIoU of 68.9% and 78.9%, respectively. The proposed PDES-Net enhances segmentation performance while maintaining advantages in model parameters and inference speed. Overall, the network achieves a well-balanced trade-off between accuracy and computational efficiency.