CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm
This work addresses robustness and semantic issues in point cloud completion for 3D vision applications, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of point cloud completion by proposing CP3, a unified Pretrain-Prompt-Predict paradigm that improves robustness and semantic awareness, resulting in outperforming state-of-the-art methods by a large margin.
Point cloud completion aims to predict complete shape from its partial observation. Current approaches mainly consist of generation and refinement stages in a coarse-to-fine style. However, the generation stage often lacks robustness to tackle different incomplete variations, while the refinement stage blindly recovers point clouds without the semantic awareness. To tackle these challenges, we unify point cloud Completion by a generic Pretrain-Prompt-Predict paradigm, namely CP3. Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively. Then, we introduce a concise self-supervised pretraining stage before prompting. It can effectively increase robustness of point cloud generation, by an Incompletion-Of-Incompletion (IOI) pretext task. Moreover, we develop a novel Semantic Conditional Refinement (SCR) network at the predicting stage. It can discriminatively modulate multi-scale refinement with the guidance of semantics. Finally, extensive experiments demonstrate that our CP3 outperforms the state-of-the-art methods with a large margin.