面向小样本3D点云语义分割的多层次全局感知模型

doi:10.16451/j.cnki.issn1003-6059.202603003

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Abstract Few-shot point cloud semantic segmentation struggles to handle complex structures, ambiguous semantics and noise interference due to its limitations in global context modeling, feature alignment and semantic guidance. To address these issues, a multi-level global-aware model for few-shot 3D point cloud semantic segmentation(MGAM) is proposed. On the basis of multi-level window partitioning, the receptive field is progressively expanded to achieve collaborative modeling of local geometry and global semantics. A dual-domain attention fusion module(DAFM) is designed. Channel attention and point-wise attention are integrated to fuse local and global information. A global auxiliary point mechanism(GAPM) is constructed. Learnable global points are embedded into key layers to enhance cross-layer feature propagation. A global category-aware loss(GCAL) is developed. Category distribution constraints are added to point-level supervision to highlight small-class targets. Ablation experiments verify the effectiveness and complementarity of each module. Comparative experiments show superior performance of MGAM, particularly in complex scenarios.

Key words： Meta-Learning Few-Shot Learning Point Cloud Semantic Segmentation Feature Fusion

Received: 16 December 2025

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.61775139)

Corresponding Authors: YUAN Jian, Ph.D., associate professor. Her research interests include image processing, natural language processing, and deep learning.

About author:: YAN Weishu, Master student. His research interests include computer vision.
YANG Mingrui, Master student. His research interests include AI-generated content.
XU Jiahui, Master student. His research interests include image processing.

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Cite this article:

YAN Weishu,YUAN Jian,YANG Mingrui等. Multi-level Global-Aware Model for Few-Shot 3D Point Cloud Semantic Segmentation[J]. Pattern Recognition and Artificial Intelligence, 2026, 39(3): 225-238.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202603003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I3/225

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