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.

一种基于学习的视可见性面重建方法，使用3D Delaunay四面体化及基于图神经网络的分类方法识别点云中缺陷区域并生成表面模型的能量模型，以局部几何和视线可见性信息学习能见度模型，结合深度学习和能量模型的优点，优于当前公开的基于学习和非学习的表面重建算法。

使用Delaunay图神经网络进行可扩展的曲面重建