Stage-Aware Interaction Network for Point Cloud Completion

Point cloud completion aims to restore full shapes of objects from partial scans, and a typical network pipeline is AutoEncoder, which has coarse-to-fine refinement modules. Although existing approaches using this kind of architecture achieve promising results, they usually neglect the usage of shallow geometry features in partial inputs and the fusion of multi-stage features in the upsampling process, which prevents network performances from further improving. Therefore, in this paper, we propose a new method with dense interactions between different encoding and decoding steps. First, we introduce the Decoupled Multi-head Transformer (DMT), which implements and integrates semantic prediction and resolution upsampling in a unified network module, which serves as a primary ingredient in our pipeline. Second, we propose an Encoding-aware Coarse Decoder (ECD) that compactly makes the top–down shape-decoding process interact with the bottom–up feature-encoding process to utilize both shallow and deep features of partial inputs for coarse point cloud generation. Third, we design a Stage-aware Refinement Group (SRG), which comprehensively understands local semantics from densely connected features across different decoding stages and gradually upsamples point clouds based on them. In general, the key contributions of our method are the DMT for joint semantic-resolution generation, the ECD for multi-scale feature fusion-based shape decoding, and the SRG for stage-aware shape refinement. Evaluations on two synthetic and three real-world datasets illustrate that our method achieves competitive performances compared with existing approaches.