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.

通过构建先验库和分析输入和先验之间的相关性，本文提出了一种弱监督框架来从未见过的类别中重建完整的 3D 形状，并通过自监督形状细化模型进一步改进了粗糙形状，实验证明我们的方法明显优于现有方法。