Deep Depth from Focus
Hazirbas C, Soyer SG, Staab MC, Leal-Taixe L, Cremers D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11363 LNCS
Pages Range: 525-541
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Perth, WA, AUS
ISBN: 9783030208929
DOI: 10.1007/978-3-030-20893-6_33
Abstract
Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose ‘Deep Depth From Focus (DDFF)’ as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method ‘DDFFNet’ achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.
Involved external institutions
Germany (DE)How to cite
APA:
Hazirbas, C., Soyer, S.G., Staab, M.C., Leal-Taixe, L., & Cremers, D. (2019). Deep Depth from Focus. In C.V. Jawahar, Hongdong Li, Konrad Schindler, Greg Mori (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 525-541). Perth, WA, AUS: Springer Verlag.
MLA:
Hazirbas, Caner, et al. "Deep Depth from Focus." Proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, Perth, WA, AUS Ed. C.V. Jawahar, Hongdong Li, Konrad Schindler, Greg Mori, Springer Verlag, 2019. 525-541.
BibTeX: Download