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

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

LiDAR-based semantic segmentation is significant in advanced autonomous driving systems. However, LiDAR point cloud data is sparse and unevenly distributed. This poses challenges to achieving accurate and efficient semantic understanding. To address the above problem, we propose a network model, PDES-Net. We design a point-wise distance encoding mechanism that introduces normalized continuous depth information as a supplementary feature into the network. The mechanism can enhance the perception of both near and distant points in the model and reduce geometric information loss. To enhance the expressive ability of point-wise prediction, we present a pointed-seg head module. This module adaptively integrates multilevel features through learnable weight coefficients. 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.