结合有序建模和空间感知的点云语义分割算法

Ordered Modeling and Spatial Perception for Point Cloud Semantic Segmentation Algorithm

摘要

针对三维点云语义分割中的无序性、几何信息缺失及边界模糊等挑战,提出一种结合有序建模和空间感知的点云语义分割算法.首先通过融合Z阶与希尔伯特空间填充曲线及其镜像变体进行多种空间填充曲线的互补有序建模,构建空间结构保持性互补的点云编码序列;然后设计动态空间编码机制,根据不同阶段任务需求自适应调整编码维度,提升空间编码效率;最后构建空间感知聚合模块,融合空间引导的局部特征传播与多层感知机全局上下文感知,实现特征高效混合学习,增强特征的空间稳定性与几何一致性.在S3DIS、ScanNet v2和ScanObjectNN 3个公开点云数据集上的实验表明,所提算法实现了高精度语义分割,有效提升了对复杂边界区域和小物体的空间感知能力和语义理解精度.

Abstract

To address the challenges of disorder,missing geometric information,and blurred boundaries in 3D point cloud semantic segmentation,this paper proposes an algorithm that combines complementary ordered modeling with spatial perception.A complementary ordered modeling module is first constructed by integrating Z-order curves,Hilbert curves,and their mirrored variants,forming spatially consistent and structurally complementary point cloud encoding sequences.Then,a dynamic spatial encoding mechanism is introduced to adaptively adjust the encoding dimensions according to the requirements of different stages,thereby improving the efficiency of spatial representation.Finally,a spatial-aware aggregation module is designed to fuse spatially guided local feature propagation with multilayer perceptron-based global context modeling,achieving efficient hybrid feature learning while enhancing spatial stability and geometric consistency.Experiments conducted on three public datasets—S3DIS,ScanNet v2,and ScanObjectNN—demonstrate that the proposed algorithm delivers high-accuracy semantic segmentation and significantly enhances spatial perception and semantic understanding in complex boundary regions and small object scenarios.

关键词

三维点云语义分割 / 空间填充曲线 / 互补有序建模 / 动态空间编码 / 空间感知聚合

Key words

3D point cloud semantic segmentation / space-filling curves / complementary ordered modeling / dynamic spatial encoding / spatial-aware aggregation

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基金资助

国家自然科学基金项目(62262036,62362043)资助;云南省兴滇英才支持计划项目(KKXY202203008)资助.