Integrating approximate depth data into dense image correspondence estimation
Kai Ruhl, Felix Klose, Christian Lipski, Marcus A. Magnor · 2012
High-quality dense image correspondence estimation between two images is an essential prerequisite for many tasks in visual media production, one prominent example being view interpolation. Due to the ill-posed nature of the correspondence estimation problem, errors occur frequently for a number of problematic conditions, among them occlusions, large displacements and low-textured regions. In this paper, we propose to use approximate depth data from low-resolution depth sensors or coarse geometric proxies to guide the high-resolution image correspondence estimation. We counteract the effect of uncertainty in the prior by exploiting the coarse-to-fine image pyramid used in our estimation algorithm. Our results show that even with only approximate priors, visual quality improves considerably compared to an unguided algorithm or a pure depth-based interpolation.