Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point clouds, which may cause the loss of details because the structural and context of point clouds are not fully considered. To solve this problem, we introduce 3D grids as intermediate representations to regularize unordered point clouds. We therefore propose a novel Gridding Residual Network (GRNet) for point cloud completion. In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information. We also present the differentiable Cubic Feature Sampling layer to extract features of neighboring points, which preserves context information. In addition, we design a new loss function, namely Gridding Loss, to calculate the L1 distance between the 3D grids of the predicted and ground truth point clouds, which is helpful to recover details. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks.

翻译：对不完整的3D点云进行估计是许多视觉和机器人应用中的一个关键问题。主流方法(例如PCN和TopNet)使用多层光谱和3D电网直接处理点云,这可能造成细节丢失,因为没有充分考虑到点云的结构和背景。为了解决这个问题,我们引入3D网格作为中间显示器,以规范未定点云。因此,我们提议为点云的完成建立一个新颖的网格残余网络(GRNet)。特别是,我们设计了两个新的不同层,称为网格和网格反转,以便在不丢失结构信息的情况下转换点云和3D电网。我们还介绍了不同的紫外线取样层以提取相邻点的特征,这些特征保存了背景信息。此外,我们设计了一个新的损失函数,即“网格损失”,以计算预测和地面真理云的3D网格之间的L1距离,这有助于恢复细节。实验结果表明,拟议的SangGRNet3运行了符合州际信息技术标准的方法。