IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Learning Pyramidal Feature Hierarchy for 3D Reconstruction
Fairuz Safwan MAHADMasakazu IWAMURAKoichi KISE
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2022 Volume E105.D Issue 2 Pages 446-449

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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.

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© 2022 The Institute of Electronics, Information and Communication Engineers
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