Browsing by Author "Marlet, Renaud"

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    Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Sulzer, Raphael; Landrieu, Loic; Marlet, Renaud; Vallet, Bruno; Digne, Julie and Crane, Keenan
    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 energybased models, our approach outperforms both learning and non learning-based reconstruction algorithms on two publicly available reconstruction benchmarks.