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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 fewer artifacts when compared with the state-of-the-arts.

Video Results


3D Photography using Context-aware Layered Depth Inpainting


Meng-Li Shih, Shih-Yang Su, Johannes Kopf, and Jia-Bin Huang. "3D Photography using Context-aware Layered Depth Inpainting", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020


  author = {Shih, Meng-Li and Su, Shih-Yang and Kopf, Johannes and Huang, Jia-Bin},
  title = {3D Photography using Context-aware Layered Depth Inpainting},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}


We thank Pratul Srinivasan for providing clarification of the method [Srinivasan et al. CVPR 2019].
We thank the author of [Zhou et al. 2018, Choi et al. 2019, Mildenhall et al. 2019, Srinivasan et al. 2019, Wiles et al. 2020, Niklaus et al. 2019] for providing their implementations online.
Part of our codes are based on MiDaS, edge-connect and pytorch-inpainting-with-partial-conv.