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|a 10.1109/TIP.2023.3321458
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|a eng
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|a You, Meng
|e verfasserin
|4 aut
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|a Learning a Locally Unified 3D Point Cloud for View Synthesis
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|c 2023
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|a Text
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|a Date Revised 20.10.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we explore the problem of 3D point cloud representation-based view synthesis from a set of sparse source views. To tackle this challenging problem, we propose a new deep learning-based view synthesis paradigm that learns a locally unified 3D point cloud from source views. Specifically, we first construct sub-point clouds by projecting source views to 3D space based on their depth maps. Then, we learn the locally unified 3D point cloud by adaptively fusing points at a local neighborhood defined on the union of the sub-point clouds. Besides, we also propose a 3D geometry-guided image restoration module to fill the holes and recover high-frequency details of the rendered novel views. Experimental results on three benchmark datasets demonstrate that our method can improve the average PSNR by more than 4 dB while preserving more accurate visual details, compared with state-of-the-art view synthesis methods. The code will be publicly available at https://github.com/mengyou2/PCVS
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|a Journal Article
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|a Guo, Mantang
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|a Lyu, Xianqiang
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| 700 |
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|a Liu, Hui
|e verfasserin
|4 aut
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| 700 |
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|a Hou, Junhui
|e verfasserin
|4 aut
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 09., Seite 5610-5622
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