Optimization over Disentangled Encoding: Unsupervised Cross-Domain Point Cloud Completion via Occlusion Factor Manipulation

Recently, studies considering domain gaps in shape completion attracted more attention, due to the undesirable performance of supervised methods on real scans. They only noticed the gap in input scans, but ignored the gap in output prediction, which is specific for completion. In this paper, we disentangle partial scans into three (domain, shape, and occlusion) factors to handle the output gap in cross-domain completion. For factor learning, we design view-point prediction and domain classification tasks in a self-supervised manner and bring a factor permutation consistency regularization to ensure factor independence. Thus, scans can be completed by simply manipulating occlusion factors while preserving domain and shape information. To further adapt to instances in the target domain, we introduce an optimization stage to maximize the consistency between completed shapes and input scans. Extensive experiments on real scans and synthetic datasets show that ours outperforms previous methods by a large margin and is encouraging for the following works. Code is available at https://github.com/azuki-miho/OptDE.