Even though obtaining 3D information has received significant attention in scene capture systems in recent years, there are currently numerous challenges within scene depth estimation which is one of the fundamental parts of any 3D vision system focusing on RGB-D images. This has lead to the creation of an area of research where the goal is to complete the missing 3D information post capture. In many downstream applications, incomplete scene depth is of limited value, and thus, techniques are required to fill the holes that exist in terms of both missing depth and colour scene information. An analogous problem exists within the scope of scene filling post object removal in the same context. Although considerable research has resulted in notable progress in the synthetic expansion or reconstruction of missing colour scene information in both statistical and structural forms, work on the plausible completion of missing scene depth is contrastingly limited. Furthermore, recent advances in machine learning using deep neural networks have enabled complete depth estimation in a monocular or stereo framework circumnavigating the need for any completion post-processing, hence increasing both efficiency and functionality. In this chapter, a brief overview of the advances in the state-of-the-art approaches within RGB-D completion is presented while noting related solutions in the space of traditional texture synthesis and colour image completion for hole filling. Recent advances in employing learning-based techniques for this and related depth estimation tasks are also explored and presented.
Atapour-Abarghouei, A., & Breckon, T. (2019). Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation. In P. L. Rosin, Y. Lai, L. Shao, & Y. Liu (Eds.), RGB-D image analysis and processing (15-50). Springer Verlag. https://doi.org/10.1007/978-3-030-28603-3_2