Scalable Surface Reconstruction with Delaunay‐Graph Neural Networks

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 t...

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Veröffentlicht in:Computer graphics forum Jg. 40; H. 5; S. 157 - 167
Hauptverfasser: Sulzer, R., Landrieu, L., Marlet, R., Vallet, B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.08.2021
Wiley
Schriftenreihe:Eurographics Symposium on Geometry Processing 2021, July 12 – 14, 2021
Schlagworte:
Algorithms
CCS Concepts
Computer Science
Computing methodologies → Reconstruction
Deep learning
Graph neural networks
Machine learning
Neural networks
Reconstruction
Shape inference
Visibility
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14364