AGFA-Net: Adaptive Global Feature Augmentation Network for Point Cloud Completion

Completing shapes of point clouds from partial scans is a fundamental problem for 3-D vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors (KNN) operations to extract local features of point clouds, which are susceptible to outliers and have limited ability to capture features from long-range context information. In this letter, we propose a new framework with an encoder-decoder architecture, named adaptive global feature augmentation network (AGFA-Net) for point cloud completion. The network mainly consists of spatial and channel attention blocks. Spatial attention blocks are used to replace KNN operations and aggregate global features adaptively by calculating per-point attention values, and channel attention blocks are used to augment useful features of geometric details. Meanwhile, several skip connections are added between different attention blocks to selectively convey geometric features from local regions of partial point clouds to the completion process. Experimental results and analyses demonstrate that our method can generate finer shapes of point clouds and outperforms other state-of-the-art methods under widely used benchmark point completion network (PCN) dataset and several terrestrial laser scanning (TLS) data.