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• W4387448075 abstract "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." @default.
• W4387448075 created "2023-10-10" @default.
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• W4387448075 date "2023-01-01" @default.
• W4387448075 modified "2023-10-15" @default.
• W4387448075 title "Learning A Locally Unified 3D Point Cloud for View Synthesis" @default.
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• W4387448075 doi "https://doi.org/10.1109/tip.2023.3321458" @default.
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