基于协同补全和分割的离群点云补全方法
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
摘要：
现有的点云补全方法大多只适用于不含噪声和野值的部分点云，在实际应用中并不总是成立。本文提出了一种端到端的网络，命名为CS-Net，以完成被噪声污染或包含离群点云。在我们的CS-Net中，完成和分割模块协同工作，相互促进，受益于我们专门设计的级联结构。在分割的帮助下，更多干净的点云被送入完成模块。我们设计了一个新的完成解码器，利用分割获得的标签与FPS一起净化点云，并利用KNN分组更好地生成。完成和分割模块交替工作，共享彼此有用的信息，以逐步提高预测质量。为了训练我们的网络，我们构建了一个数据集来模拟不完整点云包含离群值的真实情况。我们的综合实验和国家的最先进的完成方法的比较证明了我们的优越性。我们还比较了分割后完成的方案和它们的端到端融合，这也证明了我们的有效性。
英文摘要：
Most existing point cloud completion methods are only applicable to partial point clouds without any noises and outliers, which does not always hold in practice. We propose in this paper an end-to-end network, named CS-Net, to complete the point clouds contaminated by noises or containing outliers. In our CS-Net, the completion and segmentation modules work collaboratively to promote each other, benefited from our specifically designed cascaded structure. With the help of segmentation, more clean point cloud is fed into the completion module. We design a novel completion decoder which harnesses the labels obtained by segmentation together with FPS to purify the point cloud and leverages KNN-grouping for better generation. The completion and segmentation modules work alternately share the useful information from each other to gradually improve the quality of prediction. To train our network, we build a dataset to simulate the real case where incomplete point clouds contain outliers. Our comprehensive experiments and comparisons against state-of-the-art completion methods demonstrate our superiority. We also compare with the scheme of segmentation followed by completion and their end-to-end fusion, which also proves our efficacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
