Incomplete or missing data in three-dimensional (3D) models can lead to erroneous or flawed renderings, limiting their usefulness in applications such as visualization, geometric computation, and 3D printing. Conventional surface-repair techniques often fail to infer complex geometric details in missing areas. Neural networks successfully address hole-filling tasks in 2D images using inpainting techniques. The combination of surface reconstruction algorithms, guided by the model's curvature properties and the creativity of neural networks in the inpainting processes should provide realistic results in the hole completion task. In this paper, we propose a novel method entitled SR-CurvANN (Surface Reconstruction Based on Curvature-Aware Neural Networks) that incorporates neural network-based 2D inpainting to effectively reconstruct 3D surfaces. We train the neural networks with images that represent planar representations of the curvature at vertices of hundreds of 3D models. Once the missing areas have been inferred, a coarse-to-fine surface deformation process ensures that the surface fits the reconstructed curvature image. Our proposal makes it possible to learn and generalize patterns from a wide variety of training 3D models, generating comprehensive inpainted curvature images and surfaces. Experiments conducted on 959 models with several holes have demonstrated that SR-CurvANN excels in the shape completion process, filling holes with a remarkable level of realism and precision.

翻译：三维模型中的不完整或缺失数据可能导致错误或有缺陷的渲染，从而限制其在可视化、几何计算和三维打印等应用中的实用性。传统的表面修复技术往往难以推断缺失区域的复杂几何细节。神经网络已成功利用修复技术处理二维图像中的孔洞填充任务。结合表面重建算法（以模型曲率属性为指导）与神经网络在修复过程中的创造性，应在孔洞补全任务中提供逼真的结果。本文提出了一种名为SR-CurvANN（基于曲率感知神经网络的表面重建）的新方法，该方法结合基于神经网络的二维修复技术，以有效重建三维表面。我们使用数百个三维模型顶点曲率的平面表示图像来训练神经网络。在推断出缺失区域后，通过从粗到精的表面变形过程确保表面贴合重建的曲率图像。我们的方案能够从多样化的训练三维模型中学习并泛化模式，生成完整的修复曲率图像与表面。在包含多个孔洞的959个模型上进行的实验表明，SR-CurvANN在形状补全过程中表现卓越，能以极高的真实感和精确度填充孔洞。