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Learning Multi-Level Features for Improved 3D Reconstruction

Fairuz SAFWAN MAHAD, Masakazu IWAMURA, Koichi KISE

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Summary :

3D reconstruction methods using neural networks are popular and have been studied extensively. However, the resulting models typically lack detail, reducing the quality of the 3D reconstruction. This is because the network is not designed to capture the fine details of the object. Therefore, in this paper, we propose two networks designed to capture both the coarse and fine details of the object to improve the reconstruction of the detailed parts of the object. To accomplish this, we design two networks. The first network uses a multi-scale architecture with skip connections to associate and merge features from other levels. For the second network, we design a multi-branch deep generative network that separately learns the local features, generic features, and the intermediate features through three different tailored components. In both network architectures, the principle entails allowing the network to learn features at different levels that can reconstruct the fine parts and the overall shape of the reconstructed 3D model. We show that both of our methods outperformed state-of-the-art approaches.

Publication
IEICE TRANSACTIONS on Information Vol.E106-D No.3 pp.381-390
Publication Date
2023/03/01
Publicized
2022/12/08
Online ISSN
1745-1361
DOI
10.1587/transinf.2020ZDP7508
Type of Manuscript
PAPER
Category
Image Recognition, Computer Vision

Authors

Fairuz SAFWAN MAHAD
  Osaka Prefecture University
Masakazu IWAMURA
  Osaka Prefecture University
Koichi KISE
  Osaka Prefecture University

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