Yang Y, Niu C B, Wang K P,et al.Point cloud instance segmentation based on voxel density coding and class aware offset[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(6):117-125.

doi:10.16186/j.cnki.1673-9787.2023120078

Received:2023/12/29

Revised:2024/08/01

Published:2026/10/09

Point cloud instance segmentation based on voxel density coding and class aware offset

Yang Yi1,2, Niu Chunbo1,2, Wang Keping1,2, Wang Tian3

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

Abstract: Objectives To address the issues that voxelization tends to lose detailed point cloud information and that targets of different classes exhibit large variations in structure and scale， this paper proposes a voxel density coding module and a class-aware offset mechanism， which are integrated with SoftGroup++ to construct a point cloud instance segmentation model with improved performance.  Methods Specifically， during grid-based downsampling， the voxel density coding module encodes the density information of raw points within each voxel in parallel and fuses it with voxel features to enrich local details and enhance feature representation. In addition， the class-aware offset mechanism introduces category-specific offset coefficients to improve the spatial organization of points in the offset space， thereby enhancing instance center clustering. Finally， these two mechanisms are incorporated into an improved network architecture based on SoftGroup++ to build a complete point cloud instance segmentation model. Results Experimental results on the large-scale indoor dataset S3DIS and the large-scale outdoor dataset STPLS3D show that the proposed method achieves mAP/mAP50 of 58.8%/72.1% on S3DIS， with the best segmentation performance on categories such as columns， windows， doors， and sofas among the compared methods. On STPLS3D， the proposed method achieves mAP/mAP50 of 51.4%/66.8%， outperforming all compared methods.  Conclusions The proposed voxel density coding module and class-aware offset mechanism effectively improve point cloud segmentation performance while introducing negligible additional inference time. The resulting instance segmentation model achieves significant accuracy improvements in both indoor and outdoor scenarios.

Key words:3D point cloud; deep learning; instance segmentation; voxel density; offset vector