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，边缘信息（即真实连通性信息）的缺失使得点云识别具有挑战性。近期边缘感知方法将边缘建模融入网络设计，以更好地描述局部结构。尽管这些方法表明融入边缘信息具有优势，但边缘信息如何发挥作用仍不明确，导致用户难以分析其有效性。为阐明这一问题，本研究提出一种名为扩散单元（Diffusion Unit, DU）的新算法，以原理性且可解释的方式处理边缘信息，同时提供显著性能提升。首先，我们从理论上证明DU学习执行对任务有益的边缘增强与抑制；其次，通过实验观察并验证了边缘增强与抑制行为；再次，通过实证证明该行为有助于性能提升。在具有挑战性的基准数据集上进行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部件数据集上的物体部件分割任务以及S3DIS数据集上的场景分割任务中均达到了最先进性能。源代码已开源在https://github.com/martianxiu/DiffusionUnit。