论文标题
深入：学习共同占用，签名距离和正常的场地功能以进行形状修复
DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair
论文作者
论文摘要
我们介绍了DeepJoin，这是一种自动化方法，可以使用深神经网络为破裂的形状生成高分辨率的维修。进行自动形状维修的现有方法仅在对称对象上进行操作，需要完整的代理形状，或使用低分辨率的体素预测恢复形状，而低分辨率的体素则太粗糙，无法进行物理维修。我们通过从输入断裂形状中推断出相应的完整形状和断裂表面来产生高分辨率的恢复形状。我们提出了一个新颖的隐式形状表示，用于结合占用函数，签名距离函数和正常场的断裂形状修复。我们展示了使用我们的方法进行维修，以从Shapenet中进行合成的对象，Google扫描的对象数据集的3D扫描，QP文化遗产数据集中的古希腊陶器风格的对象以及真正的破裂物体。在倒角距离和正常的一致性方面，我们的表现要优于三种基线方法。与使用减法的现有方法和修复体不同，深入的修复体不会表现出表面伪像，并紧密连接到断裂形状的破裂区域。我们的代码可在以下网址提供：https：//github.com/terascale-allassing-sensing-research-studio/deepjoin。
We introduce DeepJoin, an automated approach to generate high-resolution repairs for fractured shapes using deep neural networks. Existing approaches to perform automated shape repair operate exclusively on symmetric objects, require a complete proxy shape, or predict restoration shapes using low-resolution voxels which are too coarse for physical repair. We generate a high-resolution restoration shape by inferring a corresponding complete shape and a break surface from an input fractured shape. We present a novel implicit shape representation for fractured shape repair that combines the occupancy function, signed distance function, and normal field. We demonstrate repairs using our approach for synthetically fractured objects from ShapeNet, 3D scans from the Google Scanned Objects dataset, objects in the style of ancient Greek pottery from the QP Cultural Heritage dataset, and real fractured objects. We outperform three baseline approaches in terms of chamfer distance and normal consistency. Unlike existing approaches and restorations using subtraction, DeepJoin restorations do not exhibit surface artifacts and join closely to the fractured region of the fractured shape. Our code is available at: https://github.com/Terascale-All-sensing-Research-Studio/DeepJoin.
