Depth completion of a single RGB-D image for integral imaging

Abstract

Our laboratory proposes a method for generating elemental images based on fusion of depth images and RGB images for integrated 3D display and has achieved certain results. However, due to the defects of commercial-grade depth cameras, accurate depth estimation of target geometry cannot be accomplished. We propose a deep learning method for estimating accurate depth data of geometry from a single RGB-D image for elemental image generation. Our proposed algorithm uses a deep convolutional network to infer surface normals, object masks and occlusion boundaries from a single RGB-D image as input. Then, based on these refined depth predictions combined with the input original depth image, the depth of all pixels, including the missing pixels in the original input depth image, is solved. Through experiments with various backbones in the proposed deep learning network structure, our resulting model has better completion on deep image inpainting.