Rakotosaona, Marie-Julie;
Guerrero, Paul;
Aigerman, Noam;
Mitra, Niloy;
Ovsjanikov, Maks;
(2021)
Learning Delaunay Surface Elements for Mesh Reconstruction.
In:
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 22-31).
IEEE
Preview |
PDF
learningDel.pdf - Accepted Version Download (24MB) | Preview |
Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Delaunay Surface Elements for Mesh Reconstruction |
| Event: | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Location: | ELECTR NETWORK |
| Dates: | 19 Jun 2021 - 25 Jun 2021 |
| ISBN-13: | 9781665445092 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR46437.2021.00009 |
| Publisher version: | https://doi.org/10.1109/CVPR46437.2021.00009 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Imaging Science & Photographic Technology, Computer Science |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159077 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
