19 December 2013 Learning the missing values in depth maps
Author Affiliations +
Proceedings Volume 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 904508 (2013) https://doi.org/10.1117/12.2035370
Event: International Conference on Optical Instruments and Technology (OIT2013), 2013, Beijing, China
Abstract
In this paper, we consider the task of hole filling in depth maps, with the help of an associated color image. We take a supervised learning approach to solve this problem. The model is learnt from the training set, which contain pixels that have depth values. Then we apply supervised learning to predict the depth values in the holes. Our model uses a regional Markov Random Field (MRF) that incorporates multiscale absolute and relative features (computed from the color image), and models depths not only at individual points but also between adjacent points. The experiments show that the proposed approach is able to recover fairly accurate depth values and achieve a high quality depth map.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xuanwu Yin, Guijin Wang, Chun Zhang, Qingmin Liao, "Learning the missing values in depth maps", Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 904508 (19 December 2013); doi: 10.1117/12.2035370; https://doi.org/10.1117/12.2035370
PROCEEDINGS
6 PAGES


SHARE
Back to Top