DeepMend:学习占用函数来表示修复的形状

Nikolas Lamb, Sean Banerjee, N. Banerjee
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引用次数: 3

摘要

我们提出了DeepMend,一种利用学习的占用函数重建骨折形状修复的新方法。现有的形状修复方法预测低分辨率体素化修复,或需要对称性或访问预先存在的完整oracle。我们将裂缝形状的占用表示为潜在完整形状和裂缝表面的占用的结合,我们使用神经网络将其建模为潜在代码的函数。给定来自输入断裂形状的占用样本,我们使用推理损失和新的惩罚项来估计潜在代码,以避免空的或大量的恢复。我们使用推断代码来重建恢复形状。我们展示了合成和真实扫描对象的模拟骨折的结果,以及扫描的真实骨折杯子的结果。与现有的体素方法和两种基线方法相比,我们的工作显示了最先进的精度结果,并避免了在骨折形状的非骨折区域的修复伪影。
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DeepMend: Learning Occupancy Functions to Represent Shape for Repair
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.
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