IEICE TRANSACTIONS on Information
Learning Pyramidal Feature Hierarchy for 3D Reconstruction
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.2 pp.446-449
- Publication Date
- 2022/02/01
- Publicized
- 2021/11/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020ZDL0001
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Fairuz Safwan MAHAD
Osaka Prefecture University
Masakazu IWAMURA
Osaka Prefecture University
Koichi KISE
Osaka Prefecture University
Keyword
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Fairuz Safwan MAHAD, Masakazu IWAMURA, Koichi KISE, "Learning Pyramidal Feature Hierarchy for 3D Reconstruction" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 2, pp. 446-449, February 2022, doi: 10.1587/transinf.2020ZDL0001.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020ZDL0001/_p
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@ARTICLE{e105-d_2_446,
author={Fairuz Safwan MAHAD, Masakazu IWAMURA, Koichi KISE, },
journal={IEICE TRANSACTIONS on Information},
title={Learning Pyramidal Feature Hierarchy for 3D Reconstruction},
year={2022},
volume={E105-D},
number={2},
pages={446-449},
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.},
keywords={},
doi={10.1587/transinf.2020ZDL0001},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Learning Pyramidal Feature Hierarchy for 3D Reconstruction
T2 - IEICE TRANSACTIONS on Information
SP - 446
EP - 449
AU - Fairuz Safwan MAHAD
AU - Masakazu IWAMURA
AU - Koichi KISE
PY - 2022
DO - 10.1587/transinf.2020ZDL0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - 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.
ER -
English
