Direction-guided grouping for point cloud semantic segmentation in coal mine roadways
CHENG Jian
ZHANG Shuchen
LI Heping
AN Ning
ZHAO Hailong
XIU Haixin
Abstract：Point cloud semantic segmentation in coal mine roadways is a critical technology for scene understanding in underground coal mine environments.However,the coexistence of multi-scale objects and imbalanced class samples in mine point clouds lead to poor seg-mentation performance for small-scale targets.To address the issue,a direction-guided grouping method for semantic segmentation of point clouds in coal mine roadways is introduced.Leveraging the directional distribution of typical small-scale objects(e.g.,pipes and cables),a Direction-Guided Grouping(DIG)strategy is proposed to optimize the shape of grouping envelopes in point cloud segmentation networks,thereby increasing the proportion of small-scale object points within groups and enhancing feature extraction.Based on this strategy,two directional guided grouping methods are proposed:one is Ellipsoid Query-based Direction-Guided Grouping(DIG-EQ),which preserves stronger local neighborhood relationships,and the other is Space-Filling Curve-based Direction-Guided Grouping(DIG-SFC),which offers higher computational efficiency.Both methods can be flexibly adapted to different network architectures and signific-antly improve the performance of small-scale object recognition.To mitigate class imbalance,a hybrid loss function is employed to im-prove sensitivity to underrepresented categories.A semantic segmentation dataset of coal mine roadway point clouds collected from differ-ent coal mine roadways is constructed for evaluation.The results show that the proposed method attains mean Intersection-over-Union(mIoU)of 61.84%,69.49%and 76.63%on PointNet++,PointNeXt-L,and Point Transformer V3 backbones,respectively,representing im-provements of 15.83%,6.25%and 0.35%over baseline models.For small-scale categories,mIoU reach 22.28%,28.61%and 47.30%,with relative gains of 29.99%,42.34%and 5.33%.These results demonstrate the effectiveness of the proposed approach in coal mine roadway scenarios.
Keywords：coal mine roadway3D point cloudsemantic segmentationdirection-guided groupingfusion loss function
Publication Date：2025-11-30
Online Publishing Date：2025-12-15(First online date of this platform, not the publication date of the document)
Pages：15( 67-81 )