Dense 3D object reconstruction from a single depth view
In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover...
| Main Authors: | , , , , |
|---|---|
| Formato: | Journal article |
| Publicado: |
Institute of Electrical and Electronics Engineers
2018
|