We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of point cloud defects encountered in real-life Multi-View Stereo (MVS) acquisitions. Our method relies on a 3D Delaunay tetrahedralization whose cells are classified as inside or outside the surface by a graph neural network and an energy model solvable with a graph cut. Our model, making use of both local geometric attributes and line-of-sight visibility information, is able to learn a visibility model from a small amount of synthetic training data and generalizes to real-life acquisitions. Combining the efficiency of deep learning methods and the scalability of energy based models, our approach outperforms both learning and non learning-based reconstruction algorithms on two publicly available reconstruction benchmarks.

中文翻译：

---

使用 Delaunay-Graph 神经网络进行可扩展的表面重建

我们介绍了一种新的基于学习的、可见性感知的、用于大规模缺陷点云的表面重建方法。我们的方法可以应对现实生活中多视图立体 (MVS) 采集中遇到的点云缺陷的规模和种类。我们的方法依赖于 3D Delaunay 四面体化，其细胞通过图神经网络和可通过图切割求解的能量模型分类为表面内部或外部。我们的模型利用局部几何属性和视线可见性信息，能够从少量合成训练数据中学习可见性模型，并将其推广到现实生活中。结合深度学习方法的效率和基于能量的模型的可扩展性，