Weakly-Supervised Shape Multi-Completion of Point Clouds by Structural Decomposition

The challenge of transforming partial point clouds into complete meshes still persists, with current methods facing issues like data accessibility constraint, shape preservation failure and poor robustness on real-scan data. Drawing inspiration from the structural information of objects to enhance the completion, we introduce an innovative weakly-supervised shape completion method leveraging structural decomposition without the necessity of SDFs during training. By representing objects as abstract structural frameworks and part details, our method initiates by forecasting the structure of the input partial point clouds, and individually restore each component through part decomposition completion and generation. Extracted part details are represented in images, which are porous and incomplete. Hence, we utilize a completion network to complete such details. For multiple results generation, a diffusion-based generation network is employed to generate a variety of details for the missing areas. The predicted structure and details are subsequently converted back into meshes, yielding the complete results. Since the details are depicted in images, our approach eliminates the need for SDFs during the training phase, achieving weakly-supervision. We conduct extensive comparisons on both artificial and real-scan datasets, demonstrating an average improvement of over 38.1% compared to the prior method, and achieving SOTA performance.