ProtoComp++: Diverse Point Cloud Completion With Controllable Prototype

Point cloud completion aims to reconstruct the geometry of partial point clouds captured by various sensors. Traditionally, point cloud models are trained on synthetic datasets that feature limited categories and differ significantly from real-world scenarios. This gap often causes existing methods to struggle when faced with unfamiliar categories and severe incompleteness in real-world applications. In this paper, we propose PrototypeCompletion, a novel prototype-based approach for point cloud completion. The method begins by generating rough prototypes, which are then refined with additional geometric details to make the final prediction. We introduce two distinct approaches for integrating prototypes into the network: explicit prototypes and implicit prototypes. Our approach demonstrates strong generalization capabilities, allowing it to handle point cloud completion for a variety of unseen categories beyond the training data. We demonstrate that incorporating language prompts into the training of point cloud completion models significantly expands their applicability and enhances their performance in diverse point cloud completion tasks. Furthermore, we propose a new evaluation metric and a test benchmark based on ScanNet200 and KITTI, designed to assess the model's performance in real-world scenarios and foster future research in the field. Experimental results show that our method outperforms state-of-the-art models on the existing PCN and ShapeNet34 benchmarks and also excels in various real-world settings, handling different object categories and sensor types effectively. The code will be made publicly available.