A cascaded graph convolutional network for point cloud completion

Point cloud completion represents a complex task that entails predicting the complete geometry of a 3D shape from a set representation of the partial observational points. This paper presents a novel point cloud completion method, called Cascaded Graph Convolutional Completion Network (CGCN). Our method leverages a cascaded encoder–decoder architecture to predict the missing points from the input, the partial points. These predicted missing points are then concatenated with the input points to form the complete shape. Our architecture consists of two main modules: a Multi-Level Feature Extraction Encoder (MFE) and a Folding-Refinement Decoder (FRD). The MFE is composed of the Edge Feature Extraction Module (EFE) and the Global Feature Extraction Module (GFE). The encoder initiates by using several EFEs to secure multi-level local features through the execution of graph convolutional operations on each point. It then employs a GFE to extract global features from the final level features. Consequently, the MFE aggregates features containing local information into global features, offering an output that carries both local and global features. Our FRD contains both a folding block and several refinement blocks. Initially, the folding block morphs 2D grids with features output by the MFE to predict the initial missing points. The decoder then iteratively applies a series of refinement blocks to refine these initial missing points and eventually obtain the output of the predicted missing points. The FRD merges multi-level features and global features to deliver the shape of the predicted missing part. To achieve the complete shape, we integrate the predicted missing part, derived from the FRD and the input points. We then sample this unified point cloud to secure the final output. The effectiveness and competitiveness of our model are validated through experiments on the ShapeNet dataset, using Chamfer Distance (CD) as metrics.