Research    Publications    Funding    Professor    People    Course

Martin Cadik, Daniel Sykora, and Sungkil Lee

Elsevier Computers & Graphics, 74, 109–118, 2018.
Image enhancement tasks can highly benefit from depth information, but the direct estimation of outdoor depth maps is difficult due to vast object distances. This paper presents a fully automatic framework for model-based synthesis of outdoor depth maps and its applications to image enhancements. We leverage 3D terrain models and camera pose estimation techniques to render approximate depth maps without resorting to manual alignment. Potential local misalignments, resulting from insufficient model details and rough registrations, are eliminated with our novel free-form warping. We first align synthetic depth edges with photo edges using the as-rigid-as-possible image registration and further refine the shape of the edges using the tight trimap-based alpha matting. The resulting synthetic depth maps are accurate, calibrated in the absolute distance. We demonstrate their benefit in image enhancement techniques including reblurring, depth-of-field simulation, haze removal, and guided texture synthesis.
Paper preprints, slides, supplementary materials, and Google Scholar entry
* Copyright Disclaimer: paper preprints in this page are provided only for personal academic uses, and not for redistribution.
@ARTICLE{cadik18:sdm, title={{Automated Outdoor Depth-Map Generation and Alignment}}, author={Martin Cadik and Daniel Sykora and Sungkil Lee}, journal={{Elsevier Computers \& Graphics}}, volume={74}, pages={109--118}, year={2018} }

27336, College of Software, Sungkyunkwan University, Tel. +82 31-299-4917, Seobu-ro 2066, Jangan-gu, Suwon, 16419, South Korea
Campus map (how to reach CGLab)