We propose a method for converting a single RGB-D input image into a 3D photo, i.e., a multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view. We use a Layered Depth Image with explicit pixel connectivity as underlying representation, and present a learning-based inpainting model that iteratively synthesizes new local color-and-depth content into the occluded region in a spatial context-aware manner. The resulting 3D photos can be efficiently rendered with motion parallax using standard graphics engines. We validate the effectiveness of our method on a wide range of challenging everyday scenes and show less artifacts when compared with the state-of-the-arts.

Meng-Li Shih, Shih-Yang Su, Johannes Kopf, Jia-Bin Huang: 3D Photography Using Context-Aware Layered Depth Inpainting. CVPR 2020: 8025-8035

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Publication Details

Date of publication:
August 5, 2020
Computer Vision and Pattern Recognition
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