Point cloud based shape completion has great significant application values and refers to reconstructing a complete point cloud from a partial input. In this paper, we propose a multi-stage point completion network (MSPCN) with critical set supervision. In our network, a cascade of upsampling units is used to progressively recover the high-resolution results with several stages. Different from the existing works that generate the output point cloud structure supervised by the complete ground truth, we leverage the critical set at each stage for supervision and generate a more informative and useful intermediate outputs for the next stage. We propose a strategy by combining max-pooling selected points and volume-downsampling points to determine critical sets (MVCS) for supervision, which concerns both critical features and the shape of the model. We conduct extensive experiments on the ShapeNet dataset and the experimental results clearly demonstrate that our proposed MSPCN with critical set supervision outperforms the state-of-the-art completion methods.

基于点云的形状完成具有重要的应用价值，是指从部分输入中重建完整的点云。在本文中，我们提出了一种具有关键集监督的多阶段点完成网络（MSPCN）。在我们的网络中，级联的上采样单元用于分几个阶段逐步恢复高分辨率结果。与现有的生成由完整的地面事实监督的输出点云结构的现有工作不同，我们利用每个阶段的关键集进行监督，并为下一阶段生成更有用的信息。我们提出了一种策略，通过组合最大池化选定点和体积下采样点来确定要监督的关键集（MVCS），该集涉及关键特征和模型的形状。