BI X H, NIU C B, YANG Y,et al.Point cloud instance segmentation based on voxel-based bilateral attention and octree feature encoding[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):87-96.

doi:10.16186/j.cnki.1673-9787.2025120086

Received:2025/12/24

Revised:2026/03/27

Published:2026-07-24

Point cloud instance segmentation based on voxel-based bilateral attention and octree feature encoding

Bi Xiaohua1,2,3, Niu Chunbo2,3, Yang Yi2,3, Wang Tian4

1.Jiaozuo Coal Industry Group Co.， Ltd.， Jiaozuo  454002， Henan， China；2.School of Electrical Engineering and Automation， Henan Polytechnic University， Jiaozuo  454003， Henan， China；3.Henan Key Laboratory of Intelligent Inspection and Control of Coal Mine Equipment， Henan Polytechnic University， Jiaozuo  454003， Henan， China；4.School of Artificial Intelligence， Beihang University， Beijing  100191， China

Abstract: Objectives The essence of point cloud instance segmentation is to generate a closed classification surface in 3D space through a nonlinear mapping model， so that the target point cloud can be distinguished. However， the elongated and protruding point clouds of instance objects are usually distributed as outliers， which poses a challenge to accurate point cloud segmentation. Therefore， capturing the correlation features between outlier sparse point clouds and target objects in candidate instance regions， generating precise instance masks and thereby establishing a nonlinear mapping model from positional space to classification surfaces are considered critical for accurate point cloud instance segmentation.  Methods In this paper， a miniature U-Net based on the voxel-based bilateral attention mechanism was constructed. Channel and positional attention mechanisms of point clouds were fused at the voxel level， enabling the model to focus on local positional features while adaptively readjusting feature maps by learning the importance of each channel. In this way， long-range dependencies among point clouds within candidate instances were established. Meanwhile， to address the problem of outlier sparse point cloud misclassification， an octant feature encoding module was proposed. Candidate instances were split and encoded into octants， and both the internal characteristics of each octant and the correlation features between octants were extracted. As a result， distant outlier scattered points were incorporated into the feature extraction of candidate instances.  Results Experimental results show that the proposed method achieves 51.8% mAP and 68.2% mAP50， demonstrating a significant improvement in point cloud instance segmentation performance.  Conclusions More accurate instance masks were generated by the bilateral attention mechanism and octant feature encoding module， enabling precise classification of outlier point clouds.

Key words:point cloud instance segmentation;bilateral attention;octant feature encoding;channel attention