We propose a novel Deformed Implicit Field (DIF) representation for modeling 3D shapes of a category and generating dense correspondences among shapes. With DIF, a 3D shape is represented by a template implicit field shared across the category, together with a 3D deformation field and a correction field dedicated for each shape instance. Shape correspondences can be easily established using their deformation fields. Our neural network, dubbed DIF-Net, jointly learns a shape latent space and these fields for 3D objects belonging to a category without using any correspondence or part label. The learned DIF-Net can also provides reliable correspondence uncertainty measurement reflecting shape structure discrepancy. Experiments show that DIF-Net not only produces high-fidelity 3D shapes but also builds high-quality dense correspondences across different shapes. We also demonstrate several applications such as texture transfer and shape editing, where our method achieves compelling results that cannot be achieved by previous methods.

本论文提出了Deformed Implicit Field (DIF) 表示法来建模3D物品的形状，实现物品之间稠密的对应关系。DIF-Net神经网络通过学习一个特定类别的物品的形态潜在空间和这些物品的Deformation Field来实现对应关系的建立以及对物品形状差异的度量，还考虑了材质和形状编辑等应用。

Deformed Implicit Field：学习稠密对应关系建模三维形状