Scalable Surface Reconstruction with Delaunay-Graph Neural Networks

Sulzer, Raphael; Landrieu, Loic; Marlet, Renaud; Vallet, Bruno

Scalable Surface Reconstruction with Delaunay-Graph Neural Networks

Files

v40i5pp157-167.pdf (6.99 MB)

paper1082_sulzer_sgp_2021_suppmat.pdf (29.76 MB)

Date

2021

Authors

Sulzer, Raphael
Landrieu, Loic
Marlet, Renaud
Vallet, Bruno

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.

        @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
}
}