Detail of Publication
| Text Language | English |
|---|---|
| Authors | Fairuz Safwan Mahad, Masakazu Iwamura, and Koichi Kise |
| Title | Learning Pyramidal Feature Hierarchy for 3D reconstruction |
| Journal | IEICE Transactions on Information and Systems |
| Vol. | E105-D |
| No. | 2 |
| Pages | pp.446-449 |
| Number of Pages | 4 pages |
| Publisher | IEICE |
| Reviewed or not | Reviewed |
| Month & Year | February 2022 |
| Abstract | Neural network-based three-dimensional (3D) reconstruction methods have produced promising results. However, they do not pay particular attention to reconstructing detailed parts of objects. This occurs because the network is not designed to capture the fine details of objects. In this paper, we propose a network designed to capture both the coarse and fine details of objects to improve the reconstruction of the fine parts of objects. |
| DOI | 10.1587/transinf.2020ZDL0001 |
- Following file is available.
- Entry for BibTeX
@Article{Mahad2022, author = {Fairuz Safwan Mahad and Masakazu Iwamura and Koichi Kise}, title = {Learning Pyramidal Feature Hierarchy for 3D reconstruction}, journal = {IEICE Transactions on Information and Systems}, year = 2022, month = feb, volume = {E105-D}, number = {2}, pages = {446--449}, numpages = {4}, DOI = {10.1587/transinf.2020ZDL0001}, publisher = {IEICE} }