ECG: Edge aware Point Cloud Completion with Graph Convolution
Scanned 3D point clouds for real world scenes often suffer from noise and incompletion. Observing that prior point cloud shape completion networks overlook local geometric features, we propose our ECG an Edge aware point cloud Completion network with Graph convolution, which facilitates fine grained 3D point cloud shape generation with multi scale edge features. Our ECG consists of two consecutive stages: 1) skeleton generation and 2) details refinement. Each stage is a generation sub network conditioned on the input incomplete point cloud. The first stage generates coarse skeletons to facilitate capturing useful edge features against noisy measurements. Subsequently, we design a deep hierarchical encoder with graph convolution to propagate multi scale edge features for local geometric details refinement. To preserve local geometrical details while upsampling, we propose the Edge aware Feature Expansion (EFE) module to smoothly expand/upsample point features by emphasizing their local edges. Extensive experiments show that our ECG significantly outperforms previous state of the art (SOTA) methods for point cloud completion.
IEEE Robotics and Automation Letters