论文标题
SPCNET：逐步点云完成网络
SPCNet: Stepwise Point Cloud Completion Network
论文作者
论文摘要
您将如何修理大量错过的物理物体？您可能首先恢复其全球且粗糙的形状，并逐步增加其本地细节。我们有动力模仿上述物理维修程序，以解决点云完成任务。我们为各种3D模型提出了一个新颖的逐步点云完成网络（SPCNET）。 SPCNET具有层次的底部网络体系结构。它以迭代方式实现了形状的完成，1）首先要删除粗糙结果的全局特征； 2）然后借助全球功能来渗透本地功能； 3）最终借助局部特征和粗糙的结果来渗透详细的结果。除了模拟物理修复的智慧之外，我们还新设计了一种基于周期损失％的训练策略，以增强SPCNET的概括和鲁棒性。广泛的实验清楚地表明了我们的SPCNET优于3D点云上最先进的方法，但错过了很大。
How will you repair a physical object with large missings? You may first recover its global yet coarse shape and stepwise increase its local details. We are motivated to imitate the above physical repair procedure to address the point cloud completion task. We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings. SPCNet has a hierarchical bottom-to-up network architecture. It fulfills shape completion in an iterative manner, which 1) first infers the global feature of the coarse result; 2) then infers the local feature with the aid of global feature; and 3) finally infers the detailed result with the help of local feature and coarse result. Beyond the wisdom of simulating the physical repair, we newly design a cycle loss %based training strategy to enhance the generalization and robustness of SPCNet. Extensive experiments clearly show the superiority of our SPCNet over the state-of-the-art methods on 3D point clouds with large missings.
