3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information, makes point cloud recognition challenging. Recent edge-aware methods incorporate edge modeling into network designs to better describe local structures. Although these methods show that incorporating edge information is beneficial, how edge information helps remains unclear, making it difficult for users to analyze its usefulness. To shed light on this issue, in this study, we propose a new algorithm called Diffusion Unit (DU) that handles edge information in a principled and interpretable manner while providing decent improvement. First, we theoretically show that DU learns to perform task-beneficial edge enhancement and suppression. Second, we experimentally observe and verify the edge enhancement and suppression behavior. Third, we empirically demonstrate that this behavior contributes to performance improvement. Extensive experiments and analyses performed on challenging benchmarks verify the effectiveness of DU. Specifically, our method achieves state-of-the-art performance in object part segmentation using ShapeNet part and scene segmentation using S3DIS. Our source code is available at https://github.com/martianxiu/DiffusionUnit.

翻译：三维点云是连续表面的离散采样，可用于多种应用场景。然而，由于缺乏真实的连通性信息（即边缘信息），点云识别任务面临挑战。近期基于边缘感知的方法将边缘建模融入网络设计，以更精确地描述局部结构。尽管这些方法表明融合边缘信息具有优势，但边缘信息的具体作用机制仍不明确，使得用户难以分析其有效性。为阐明该问题，本文提出一种名为扩散单元（DU）的新算法，该算法能以规范化且可解释的方式处理边缘信息，同时显著提升性能。首先，我们从理论上证明DU能够学习执行有利于任务目标的边缘增强与抑制；其次，通过实验观察并验证了边缘增强与抑制行为；再次，实证证明该行为对性能提升具有贡献。在多个挑战性基准数据集上的大量实验与分析验证了DU的有效性。具体而言，我们的方法在ShapeNet部件数据集的对象部件分割任务和S3DIS场景分割任务中均达到了最先进的性能。源代码已开源至https://github.com/martianxiu/DiffusionUnit。