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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 multi-level features for improved 3D reconstruction
論文誌名 IEICE Transactions on Information and Systems
Vol. E106-D
No. 3
ページ数 9 pages
出版社 IEICE
査読の有無
年月 2023年3月
要約 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.
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