3D Photography using Context-aware Layered Depth Inpainting

Title: 3D Photography using Context-aware Layered Depth Inpainting

Teams: Virginia Tech   2National Tsing Hua University   3Facebook

Writers: Meng-Li Shih1,2 Shih-Yang Su1 Johannes Kopf3 Jia-Bin Huang1

Publication date: Apr 2020

Abstract

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.