Edge-guided generative network with attention for point cloud completion

Point clouds acquired through 3D scanning devices often suffer from sparsity and incompleteness due to reflection, device resolution, and viewing angle limitations. Therefore, the recovery of the complete shape from partial observations plays a vital role in assisting downstream tasks. Existing point cloud completion networks mostly ignore the encoding of the local region structure in the point cloud. In this work, we propose an edge-guided generative network with attention for point cloud completion. Specifically, the network has three consecutive stages. In the feature extraction stage, we propose the edge attention(EA) block, which can be stacked and applied to effectively capture local geometric details and structural information. The local neighborhood information is dynamically calculated, and the attention mechanism further deepens the relationship between the acquired edge features and position coordinates. We design the deconvolution attention skeleton generation module in the skeleton generation stage to generate a shape skeleton. For the detail refinement stage, we design a layered encoder based on PointNet++ module, which can better fuse the local geometry from the coarse point cloud and the global feature from the input point cloud to facilitate fine-grained point cloud generation. Comprehensive evaluations of several benchmarks indicate the effectiveness of our network and its ability to generate fine-grained point clouds.