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Enhancing point cloud analysis via neighbor aggregation correction based on cross-stage structure correlation

2025-06-18 06:08:17
Jiaqi Shi, Jin Xiao, Xiaoguang Hu, Boyang Song, Hao Jiang, Tianyou Chen, Baochang Zhang

Abstract

Point cloud analysis is the cornerstone of many downstream tasks, among which aggregating local structures is the basis for understanding point cloud data. While numerous works aggregate neighbor using three-dimensional relative coordinates, there are irrelevant point interference and feature hierarchy gap problems due to the limitation of local coordinates. Although some works address this limitation by refining spatial description though explicit modeling of cross-stage structure, these enhancement methods based on direct geometric structure encoding have problems of high computational overhead and noise sensitivity. To overcome these problems, we propose the Point Distribution Set Abstraction module (PDSA) that utilizes the correlation in the high-dimensional space to correct the feature distribution during aggregation, which improves the computational efficiency and robustness. PDSA distinguishes the point correlation based on a lightweight cross-stage structural descriptor, and enhances structural homogeneity by reducing the variance of the neighbor feature matrix and increasing classes separability though long-distance modeling. Additionally, we introducing a key point mechanism to optimize the computational overhead. The experimental result on semantic segmentation and classification tasks based on different baselines verify the generalization of the method we proposed, and achieve significant performance improvement with less parameter cost. The corresponding ablation and visualization results demonstrate the effectiveness and rationality of our method. The code and training weight is available at: this https URL

Abstract (translated)

点云分析是许多下游任务的基础,其中聚合局部结构是理解点云数据的基本步骤。尽管有许多工作利用三维相对坐标来聚合同邻近的点,但由于局部坐标的限制,这些问题会导致无关点干扰和特征层次差距的问题。虽然有一些研究通过显式建模跨阶段结构来改进空间描述以解决这些局限性,但基于直接几何结构编码的方法仍然面临着计算开销大及对噪声敏感等问题。 为了克服这些问题,我们提出了一种名为“点分布集抽象”(Point Distribution Set Abstraction, PDSA)模块的解决方案。该模块利用高维空间中的相关性来校正聚合过程中的特征分布,从而提高计算效率和鲁棒性。PDSA通过一个轻量级的跨阶段结构描述符区分不同点之间的关联,并通过长距离建模降低邻近特征矩阵方差及增加类别间的分离度以增强结构同质性。此外,我们引入了一种关键点机制来优化计算开销。 基于不同的基线在语义分割和分类任务上的实验结果验证了所提出方法的泛化能力,并且即使参数成本较低也能实现显著性能提升。相应的消融分析和可视化结果也证实了我们方法的有效性和合理性。相关代码及训练权重可在以下链接获得:[提供具体URL]

URL

https://arxiv.org/abs/2506.15160

PDF

https://arxiv.org/pdf/2506.15160.pdf


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