3D shapes captured by scanning devices are often incomplete due to occlusion. 3D shape completion methods have been explored to tackle this limitation. However, most of these methods are only trained and tested on a subset of categories, resulting in poor generalization to unseen categories. In this paper, we introduce a novel weakly-supervised framework to reconstruct the complete shapes from unseen categories. We first propose an end-to-end prior-assisted shape learning network that leverages data from the seen categories to infer a coarse shape. Specifically, we construct a prior bank consisting of representative shapes from the seen categories. Then, we design a multi-scale pattern correlation module for learning the complete shape of the input by analyzing the correlation between local patterns within the input and the priors at various scales. In addition, we propose a self-supervised shape refinement model to further refine the coarse shape. Considering the shape variability of 3D objects across categories, we construct a category-specific prior bank to facilitate shape refinement. Then, we devise a voxel-based partial matching loss and leverage the partial scans to drive the refinement process. Extensive experimental results show that our approach is superior to state-of-the-art methods by a large margin.

翻译：扫描设备捕获的3D形状常因遮挡而不完整。已有研究者探索了3D形状补全方法以解决此局限。然而，大多数方法仅在部分类别上训练和测试，导致对未见类别的泛化能力较差。本文提出一种新颖的弱监督框架，用于从未见类别重建完整形状。我们首先设计了一个端到端的先验辅助形状学习网络，利用可见类别的数据推断粗糙形状。具体而言，我们构建了一个由可见类别代表性形状组成的先验库。随后设计多尺度模式关联模块，通过分析输入中局部模式与不同尺度先验之间的关联来学习完整形状。此外，我们提出一种自监督形状优化模型以进一步细化粗糙形状。考虑不同类别3D物体的形状变异性，我们构建了类别特异性先验库以辅助形状优化。接着设计了一种基于体素的部分匹配损失函数，利用部分扫描数据驱动优化过程。大量实验结果表明，我们的方法显著优于现有最先进方法。