Scalable Surface Reconstruction with Delaunay-Graph Neural Networks

• URL: http://arxiv.org/abs/2107.06130v2
• Date: Thu, 15 Jul 2021 16:01:59 GMT
• Title: Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
• Authors: Raphael Sulzer, Loic Landrieu, Renaud Marlet, Bruno Vallet
• Abstract summary: 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.
• Score: 14.128976778330474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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