Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
| Autor: | Sulzer, Raphael, Landrieu, Loic, Marlet, Renaud, Vallet, Bruno |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Computer Graphics Forum 2021 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1111/cgf.14364 |
| Popis: | 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. Our code and data is available at https://github.com/raphaelsulzer/dgnn. Comment: The presentation of this work at SGP 2021 is available at https://youtu.be/KIrCDGhS10o |
| Databáze: | arXiv |
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