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|a 10.1109/TPAMI.2023.3242709
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|a eng
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| 100 |
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|a Guo, Mantang
|e verfasserin
|4 aut
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|a Content-Aware Warping for View Synthesis
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|c 2023
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|a Date Completed 03.07.2023
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|a Date Revised 03.07.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the traditional warping operation to be the limited neighborhood and only distance-based interpolation weights. To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from a set of input source views, in which two additional modules, namely confidence-based blending and feature-assistant spatial refinement, are naturally proposed to handle the occlusion issue and capture the spatial correlation among pixels of the synthesized view, respectively. Besides, we also propose a weight-smoothness loss term to regularize the network. Experimental results on light field datasets with wide baselines and multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually. The source code is publicly available at https://github.com/MantangGuo/CW4VS
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|a Journal Article
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1 |
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|a Hou, Junhui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jin, Jing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Hui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zeng, Huanqiang
|e verfasserin
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| 700 |
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|a Lu, Jiwen
|e verfasserin
|4 aut
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|t IEEE transactions on pattern analysis and machine intelligence
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|g 45(2023), 8 vom: 06. Aug., Seite 9486-9503
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|u http://dx.doi.org/10.1109/TPAMI.2023.3242709
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