Deep Multi Depth Panoramas for View Synthesis

• URL: http://arxiv.org/abs/2008.01815v1
• Date: Tue, 4 Aug 2020 20:29:15 GMT
• Title: Deep Multi Depth Panoramas for View Synthesis
• Authors: Kai-En Lin, Zexiang Xu, Ben Mildenhall, Pratul P. Srinivasan, Yannick Hold-Geoffroy, Stephen DiVerdi, Qi Sun, Kalyan Sunkavalli, and Ravi Ramamoorthi
• Abstract summary: We present a novel scene representation - Multi Depth Panorama (MDP) - that consists of multiple RGBD$alpha$ panoramas. MDPs are more compact than previous 3D scene representations and enable high-quality, efficient new view rendering.
• Score: 70.9125433400375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs. Previous work constructs RGBD panoramas from such data, allowing for view synthesis with small amounts of translation, but cannot handle the disocclusions and view-dependent effects that are caused by large translations. To address this issue, we present a novel scene representation - Multi Depth Panorama (MDP) - that consists of multiple RGBD$\alpha$ panoramas that represent both scene geometry and appearance. We demonstrate a deep neural network-based method to reconstruct MDPs from multi-camera 360$^{\circ}$ images. MDPs are more compact than previous 3D scene representations and enable high-quality, efficient new view rendering. We demonstrate this via experiments on both synthetic and real data and comparisons with previous state-of-the-art methods spanning both learning-based approaches and classical RGBD-based methods.

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