Learning Dense Correspondences for Non-Rigid Point Clouds With Two-Stage Regression
We propose a novel deep learning method to predict dense correspondences for partial point clouds of non-rigidly deformable targets. Dense correspondences are learned in the form of vertex displacements of a template mesh towards the point clouds. A two-stage regression framework is proposed to esti...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 15., Seite 8468-8482 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2021
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Accès en ligne |
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