PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation

Abstract

This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative methods do not have enough training data and representation capacity to synthesize plausible, fine-grained details with complex geometry and topology. Thus, our key insight is to copy and deform the patches from the partial input to complete the missing regions. This enables us to preserve the style of local geometric features, even if it is drastically different from the training data. Our fully automatic approach proceeds in two stages. First, we learn to retrieve candidate patches from the input shape. Second, we select and deform some of the retrieved candidates to seamlessly blend them into the complete shape. This method combines the advantages of the two most common completion methods: similarity-based single-instance completion, and completion by learning a shape space. We leverage repeating patterns by retrieving patches from the partial input, and learn global structural priors by using a neural network to guide the retrieval and deformation steps. Experimental results show that our approach considerably outperforms baseline approaches across multiple datasets and shape categories.