文献の詳細
| 論文の言語 | 英語 |
|---|---|
| 著者 | Fairuz Safwan Mahad, Masakazu Iwamura, and Koichi Kise |
| 論文名 | Learning Pyramidal Feature Hierarchy for 3D reconstruction |
| 論文誌名 | IEICE Transactions on Information and Systems |
| Vol. | E105-D |
| No. | 2 |
| ページ | pp.446-449 |
| ページ数 | 4 pages |
| 出版社 | IEICE |
| 査読の有無 | 有 |
| 年月 | 2022年2月 |
| 要約 | 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 |
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- BibTeX用エントリー
@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} }