Learning Delaunay Surface Elements for Mesh Reconstruction
| Autor: | Marie-Julie Rakotosaona, Noam Aigerman, Maks Ovsjanikov, Paul Guerrero, Niloy J. Mitra |
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| Rok vydání: | 2020 |
| Předmět: |
Surface (mathematics)
FOS: Computer and information sciences Geodesic Delaunay triangulation Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Point cloud ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science::Computational Geometry Manifold Graphics (cs.GR) Computer Science - Graphics Computer Science::Graphics Projection (mathematics) Point (geometry) Polygon mesh Artificial intelligence business Algorithm ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR |
| DOI: | 10.48550/arxiv.2012.01203 |
| Popis: | We present a method for reconstructing triangle meshes from point clouds. Existing learning-based methods for mesh reconstruction mostly generate triangles individually, making it hard to create manifold meshes. We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements. Our method first estimates local geodesic neighborhoods around each point. We then perform a 2D projection of these neighborhoods using a learned logarithmic map. A Delaunay triangulation in this 2D domain is guaranteed to produce a manifold patch, which we call a Delaunay surface element. We synchronize the local 2D projections of neighboring elements to maximize the manifoldness of the reconstructed mesh. Our results show that we achieve better overall manifoldness of our reconstructed meshes than current methods to reconstruct meshes with arbitrary topology. Our code, data and pretrained models can be found online: https://github.com/mrakotosaon/dse-meshing |
| Databáze: | OpenAIRE |
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