3D Photography using Context-aware Layered Depth Inpainting

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Authors Johannes Kopf, Shih-Yang Su, Jia-Bin Huang, Meng-Li Shih
Journal/Conference Name CVPR 2020 6
Paper Category
Paper Abstract We propose a method for converting a single RGB-D input image into a 3D photo - 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 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 fewer artifacts compared with the state of the arts.
Date of publication 2020
Code Programming Language Python
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