A Statistical Model for Disocclusions in Depth-based Novel View Synthesis

The occurrence of missing regions in output images is a critical issue when rendering a scene from novel vantage points using depth-based view synthesis. These regions typically have to be filled using inpainting algorithms, which are slow and might yield unconvincing results. Understanding the likelihood of the occurrence of these missing regions can help us design better, application-specific data representations and camera systems by knowing which vantage points should be captured and stored to minimize disocclusion holes in the synthesized novel views. In this paper, we propose a statistical model that predicts the likelihood of missing data in synthesized images as a function of the viewpoint translation. Scene-dependent model parameters are efficiently estimated using simple shift and scaling transformations on the source depth images without needing view synthesis.