YANG Y, XUE X J, WANG T,et al.Semantic segmentation of three-dimensional point cloud based on bilateral feature difference aggregation and dilated residuals[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(3):30-40.

doi:10.16186/j.cnki.1673-9787.2023090059

Received:2025/06/27

Revised:2025/10/13

Published:2026/04/28

Semantic segmentation of three-dimensional point cloud based on bilateral feature difference aggregation and dilated residuals

Yang Yi1,2, Xue Xiaojie1,2, Wang Tian3, Cui Kefei4, Fei Shumin5

1.School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo  454003， Henan， China；2.Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment， Henan Polytechnic University， Jiaozuo  454003， Henan， China；3.Institute of Artificial Intelligence， Beihang University， Beijing  100191， China；4.Zhengzhou Hengda Intelligent Control Technology Company Limited， Zhengzhou  450016， Henan， China；5.School of Automation， Southeast University， Nanjing  210096， Jiangsu， China

Abstract: Objectives Point cloud semantic segmentation was the foundation of understanding and analyzing point cloud scenes. In order to solve the problem that the existing point cloud semantic segmentation network did not extract features sufficiently， which leads to the inability to effectively obtain local context information and poor performance in segmenting complex shaped point clouds. Thus， a point cloud semantic segmentation network based on bilateral feature difference aggregation and dilated residual was proposed.  Methods Firstly， the geometric coordinates and semantic features of the local point cloud were differentially encoded and adjusted. This allowed the network to comprehensively learn the spatial relationships between points and fully explore the connections between different types of features， thereby enhancing local contextual information. Then， the salient and significant features were aggregated by using difference mixed pooling to reduce information loss and thus obtained an accurate single-point feature representation. Finally， by expanding the residual module， the network depth was deepened by the dilated residual module to expand ， the local receptive field was expanded， and obtain a larger range of contextual information was obtained.  Results Experimental validation was conducted on the large-scale indoor scene point cloud dataset S3DIS and the outdoor complex road scene point cloud dataset Toronto3D. The results indicated that the proposed algorithm had achieved the mean intersection over union of 71.6% and the mean accuracy of 83.2% on the S3DIS dataset， which was 1.6% and 1.2% improvement over RandLA-Net， respectively. On the Toronto3D dataset， the mean intersection over union and overall accuracy reached 81.0% and 96.8%， respectively， representing improvements of 4.7% and 1.3% compared to RandLA-Net. Overall， it outperformed the compared point cloud semantic segmentation methods and achieved state-of-the-artoptimal results across multiple categories in both datasets.  Conclusions In segmenting complex-shaped point clouds， our method can sufficiently extract differential feature information， accurately delineate point cloud boundaries， and achieve semantic segmentation of point clouds in different scenesscenarios.

Key words:three-dimensional point cloud;deep learning;semantic segmentation;difference aggregation;residual network