The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kaedah pembinaan semula tiga dimensi (3D) berasaskan rangkaian saraf telah menghasilkan hasil yang menjanjikan. Walau bagaimanapun, mereka tidak memberi perhatian khusus untuk membina semula bahagian terperinci objek. Ini berlaku kerana rangkaian tidak direka bentuk untuk menangkap butiran halus objek. Dalam makalah ini, kami mencadangkan rangkaian yang direka untuk menangkap kedua-dua butiran kasar dan halus objek untuk meningkatkan pembinaan semula bahagian halus objek.
Fairuz Safwan MAHAD
Osaka Prefecture University
Masakazu IWAMURA
Osaka Prefecture University
Koichi KISE
Osaka Prefecture University
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Salinan
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
Salinan
@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},}
Salinan
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 -