论文标题
DeepMend：学习占用功能以代表修复形状
DeepMend: Learning Occupancy Functions to Represent Shape for Repair
论文作者
论文摘要
我们提出了DeepMend，这是一种新型的方法，可以使用学到的占用功能重建修复术以破裂的形状。现有的形状维修方法可以预测低分辨率的体脱氧修复体，或者需要对称或访问预先存在的完整甲骨文。我们表示骨折的占用率是基础完整形状和断裂表面的占用的结合，我们将其模拟为使用神经网络的潜在代码的函数。鉴于输入骨折形状的占用样品，我们使用推理损失估算了潜在代码，并以新颖的惩罚术语增强，以避免空虚或大量的修复。我们使用推断的代码来重建修复形状。我们通过模拟裂缝在合成和现实世界扫描的物体以及扫描的实际断裂杯子上显示了结果。与现有的体素方法和两种基线方法相比，我们的工作显示出最先进的能力，并避免了骨折形状的非骨折区域的恢复伪像。
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.
