SPAC-Net: Rethinking Point Cloud Completion with Structural Prior.

Point cloud completion aims to infer a complete shape from its partial observation. Many approaches utilize a pure encoderdecoder paradigm in which complete shape can be directly predicted by shape priors learned from partial scans, however, these methods suffer from the loss of details inevitably due to the feature abstraction issues. In this paper, we propose a novel framework, termed SPAC-Net, that aims to rethink the completion task under the guidance of a new structural prior, we call it interface.