FU X Y，LI S D，FU Y P，et al. A 3D point cloud semantic segmentation algorithm based on local dual attention[J]. Journal of Henan Polytechnic University( Natural Science), doi: 10.16186/i.enki.1673-9787.2024110038.

Received：2024-11-20

Revised：2025-02-25

Online：2025-04-15

A 3D point cloud semantic segmentation algorithm based on local dual attention (Online)

FU Xiaoyue¹，LI Shaoda¹，FU Yeping²，YANG Ronghao¹，Tan Junxiang¹，Hu Guyue¹，

Lan Qinglong¹

（1. College of Earth and Planetary Sciences，Chengdu University of Technology，Chengdu， 610059，Sichuan，China；2. Sichuan Natural Resources Mapping and Geographic Information Co.， Ltd.，Chengdu 610059，Sichuan，China）

Abstract: Objectives To improve the recognition accuracy and segmentation completeness of 3D point cloud semantic segmentation for objects that are spatially close and share similar geometric structures but belong to different semantic categories, this study investigates the mechanisms of local feature extraction and contextual information fusion in point cloud semantic segmentation methods. Methods It proposes a novel 3D point cloud semantic segmentation method based on dual local attention, termed Dual Local Attention-Residual Network (DLA-ResNet). The method designs a relative distance-based encoding module to establish the relative positional relationships among points within a neighborhood, introduces a neighborhood-enhanced self-attention module to enhance the feature representation of different semantic categories within the neighborhood, improves the local attention pooling module to adaptively aggregate salient features, and utilizes a residual connection structure to fuse contextual information and obtain global features, thereby enhancing the network's ability to distinguish objects with similar features but different categories. To validate the effectiveness of the proposed method, experiments were conducted on the self-constructed CDUT-Road dataset as well as the public Toronto3D and S3DIS datasets. Results The experimental results show that the proposed DLA-ResNet method achieves mean Intersection over Union (mIoU) values of 67.4%, 78.8%, and 73.7% on the CDUT-Road, Toronto3D, and S3DIS datasets, respectively, representing improvements of 5.6%, 1.1%, and 3.7% compared to RandLA-Net. Conclusions The proposed DLA-ResNet method enhances the model's ability to process point cloud data in complex scenes by introducing dual local attention mechanisms and residual connection structures, significantly improving segmentation accuracy. The method demonstrates excellent segmentation performance on both indoor and outdoor point cloud data and exhibits robust performance across different datasets.

Key words：MLS LiDAR point cloud; semantic segmentation; deep learning; attention mechanism; neighborhood enhanced

CLC: TP391.41