Scalable Surface Reconstruction with Delaunay-Graph Neural Networks
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
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 energybased models, our approach outperforms both learning and non learning-based reconstruction algorithms on two publicly available reconstruction benchmarks.
Description
@article{10.1111:cgf.14364,
journal = {Computer Graphics Forum},
title = {{Scalable Surface Reconstruction with Delaunay-Graph Neural Networks}},
author = {Sulzer, Raphael and Landrieu, Loic and Marlet, Renaud and Vallet, Bruno},
year = {2021},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14364}
}