Pix2Repair：从图像进行隐式形状修复
Pix2Repair: Implicit Shape Restoration from Images
AI总结：
Pix2Repair从断裂物体图像自动生成可3D打印的修复形状，引入新形状函数和含11,653张图像的数据集，优于形状补全方法。
摘要：
我们提出了Pix2Repair，一种自动化的形状修复方法，该方法从图像生成修复形状以修复断裂物体。先前的修复方法需要将断裂物体的高分辨率水密三维网格作为输入。输入的三维网格必须使用昂贵的3D扫描仪获得，且扫描的网格需要手动清理，这限制了其可访问性和可扩展性。Pix2Repair将断裂物体的图像作为输入，并自动生成可3D打印的修复形状。我们贡献了一种新颖的形状函数，该函数将表示断裂物体的潜在代码分解为完整形状和断裂表面。我们还引入了Fantastic Breaks Imaged，这是首个大规模数据集，包含11,653张断裂物体的真实世界图像，用于训练和评估基于图像的形状修复方法。我们的数据集包含来自Fantastic Breaks的物体图像，并带有丰富的注释。我们展示了来自我们数据集的真实断裂以及来自Geometric Breaks和Breaking Bad数据集的合成断裂的修复结果。在倒角距离、法线一致性和生成的修复百分比方面，我们的方法优于为形状修复而改编的形状补全方法。
英文摘要：
We present Pix2Repair, an automated shape repair approach that generates restoration shapes from images to repair fractured objects. Prior repair approaches require a high-resolution watertight 3D mesh of the fractured object as input. Input 3D meshes must be obtained using expensive 3D scanners, and scanned meshes require manual cleanup, limiting accessibility and scalability. Pix2Repair takes an image of the fractured object as input and automatically generates a 3D printable restoration shape. We contribute a novel shape function that deconstructs a latent code representing the fractured object into a complete shape and a break surface. We also introduce Fantastic Breaks Imaged, the first large-scale dataset of 11,653 real-world images of fractured objects for training and evaluating image-based shape repair approaches. Our dataset contains images of objects from Fantastic Breaks, complete with rich annotations. We show restorations for real fractures from our dataset, and for synthetic fractures from the Geometric Breaks and Breaking Bad datasets. Our approach outperforms shape completion approaches adapted for shape repair in terms of chamfer distance, normal consistency, and percent restorations generated.
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