In this paper, we present ShapeMaker, a unified self-supervised learning
framework for joint shape canonicalization, segmentation, retrieval and
deformation. Given a partially-observed object in an arbitrary pose, we first
canonicalize the object by extracting point-wise affine-invariant features,
disentangling inherent structure of the object with its pose and size. These
learned features are then leveraged to predict semantically consistent part
segmentation and corresponding part centers. Next, our lightweight retrieval
module aggregates the features within each part as its retrieval token and
compare all the tokens with source shapes from a pre-established database to
identify the most geometrically similar shape. Finally, we deform the retrieved
shape in the deformation module to tightly fit the input object by harnessing
part center guided neural cage deformation. The key insight of ShapeMaker is
the simultaneous training of the four highly-associated processes:
canonicalization, segmentation, retrieval, and deformation, leveraging
cross-task consistency losses for mutual supervision. Extensive experiments on
synthetic datasets PartNet, ComplementMe, and real-world dataset Scan2CAD
demonstrate that ShapeMaker surpasses competitors by a large margin. Codes will
be released soon.

通过自监督学习，本文提出了 ShapeMaker，一个统一的形状规范化、分割、检索和变形的框架，通过提取点级别的仿射不变特征来规范化对象，预测语义一致的部分分割和对应的中心，聚合特征进行形状检索，然后使用神经笼变形将目标物体与检索到的形状进行紧密拟合，实验证明 ShapeMaker 在各个任务上超过了竞争对手。