Learning A Locally Unified 3D Point Cloud for View Synthesis

• URL: http://arxiv.org/abs/2209.05013v3
• Date: Sat, 30 Sep 2023 13:11:29 GMT
• Title: Learning A Locally Unified 3D Point Cloud for View Synthesis
• Authors: Meng You, Mantang Guo, Xianqiang Lyu, Hui Liu, and Junhui Hou
• Abstract summary: We propose a new deep learning-based view synthesis paradigm that learns a locally unified 3D point cloud from source views. Experimental results on three benchmark datasets demonstrate that our method can improve the average PSNR by more than 4 dB.
• Score: 45.757280092357355
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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