Large-Scale 3D Point Cloud Semantic Segmentation Based on GRU Feature Fusion
ZHANG Guangxin
SHUAI Hui
LIU Qingshan
Abstract：Aiming at the problem that it is difficult to effectively utilize the global and multi-level feature information in the ex-isting large-scale 3D point cloud semantic segmentation method,in order to make the model perceive more abundant feature infor-mation,this paper proposes a large-scale 3D point cloud semantic segmentation method based on the gate cycle unit.In the large-scale 3D point cloud semantic segmentation network,the gate recurrent unit(GRU)module is used for feature fusion,and the multi-level and global feature information is effectively used for semantic segmentation tasks.At the same time,the soft-max-based softpool pooling operation is used in the pooling layer.Feature fusion is performed on the global feature information.Ex-periments show that this method increases the mIoU of semantic segmentation by 1.0%and 0.5%on the S3DIS and SemanticKITTI datasets,respectively.
Keywords：large scale 3D point cloudsemantic segmentationGRUfeature fusion
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3412-3417 )