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 show restorations for synthetic fractures from the Geometric Breaks and Breaking Bad datasets, and cultural heritage objects from the QP dataset, and for real fractures from the Fantastic Breaks dataset. We overcome challenges in restoring axially symmetric objects by predicting view-centered restorations. Our approach outperforms shape completion approaches adapted for shape repair in terms of chamfer distance, earth mover's distance, normal consistency, and percent restorations generated.

翻译：我们提出了一种名为Pix2Repair的自动形状修复方法，该方法能够从图像中生成修复形状，用于修补破损物体。先前的修复方法需要将破损物体的高分辨率水密三维网格作为输入，而输入的三维网格必须通过昂贵的三维扫描仪获取，且扫描后的网格需要手动清理，这限制了其可访问性和可扩展性。Pix2Repair以破损物体的图像为输入，自动生成可进行三维打印的修复形状。我们提出了一种新颖的形状函数，该函数将表示破损物体的隐式编码解构为完整形状和断裂面。我们展示了针对Geometric Breaks和Breaking Bad数据集中的合成破损、QP数据集中的文化遗产物体以及Fantastic Breaks数据集中的真实破损的修复结果。通过预测以视角为中心的修复，我们克服了轴对称物体修复中的挑战。在倒角距离、推土机距离、法向一致性及生成的修复百分比等方面，我们的方法优于那些为形状修复而改造的形状补全方法。