We present DeepMend, a novel approach to reconstruct restorations to
fractured shapes using learned occupancy functions. Existing shape repair
approaches predict low-resolution voxelized restorations, or require symmetries
or access to a pre-existing complete oracle. We represent the occupancy of a
fractured shape as the conjunction of the occupancy of an underlying complete
shape and the fracture surface, which we model as functions of latent codes
using neural networks. Given occupancy samples from an input fractured shape,
we estimate latent codes using an inference loss augmented with novel penalty
terms that avoid empty or voluminous restorations. We use inferred codes to
reconstruct the restoration shape. We show results with simulated fractures on
synthetic and real-world scanned objects, and with scanned real fractured mugs.
Compared to the existing voxel approach and two baseline methods, our work
shows state-of-the-art results in accuracy and avoiding restoration artifacts
over non-fracture regions of the fractured shape.

本文提出了 DeepMend，一种使用学习占用函数重构断裂形状的新方法，其中断裂表面的占用被建模为神经网络的潜在代码函数，根据输入断裂形状的占用样本，利用惩罚项进行推断损失，重构了修复形状。与现有方法相比，DeepMend 在精度和避免修复的非破损区域中的遗留问题方面展现了最先进的结果。