Hierarchical Surface Prediction
Recently, Convolutional Neural Networks have shown promising results for 3D geometry prediction. They can make predictions from very little input data such as a single color image. A major limitation of such approaches is that they only predict a coarse resolution voxel grid, which does not capture...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 42(2020), 6 vom: 04. Juni, Seite 1348-1361 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2020
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Résumé: | Recently, Convolutional Neural Networks have shown promising results for 3D geometry prediction. They can make predictions from very little input data such as a single color image. A major limitation of such approaches is that they only predict a coarse resolution voxel grid, which does not capture the surface of the objects well. We propose a general framework, called hierarchical surface prediction (HSP), which facilitates prediction of high resolution voxel grids. The main insight is that it is sufficient to predict high resolution voxels around the predicted surfaces. The exterior and interior of the objects can be represented with coarse resolution voxels. This allows us to predict significantly higher resolution voxel grids around the surface, from which triangle meshes can be extracted. Additionally it allows us to predict properties such as surface color which are only defined on the surface. Our approach is not dependent on a specific input type. We show results for geometry prediction from color images and depth images. Our analysis shows that our high resolution predictions are more accurate than low resolution predictions |
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| Description: | Date Revised 11.05.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1939-3539 |
| DOI: | 10.1109/TPAMI.2019.2896296 |