Abstract
In this paper, we present a parallax-robust hexahedral panoramic video stitching method. An efficient three-stage stitching procedure is proposed. In the preprocessing stage, layered feature points matching strategy extracts feature matches lying in different depth layers. In the rough alignment stage, based on the first layer of feature matches, global projective warping estimates and refines camera parameters by considering the constraints among all cameras to avoid accumulated errors. With camera parameters, image pixels are roughly mapped onto a spherical surface. In the refined alignment stage, based on multiple layers of feature matches, layered content-preserving warping further aligns abundant feature pairs exploited from multiple depth layers, so as to alleviate ghosting caused by large parallax. Experimental results show that our method can effectively stitch panoramic video without noticeable parallax errors.
S. Guo—Thanks to National Natural Science Foundation of China 61672063, 61370115, China 863 project of 2015AA015905, Shenzhen Peacock Plan, Shenzhen Research Projects of JCYJ20160506172227337 and GGFW2017041215130858, and Guangdong Province Projects of 2014B010117007 and 2014B090910001 for funding.
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Guo, S., Wang, R., Jiang, X., Wang, Z., Gao, W. (2018). Parallax-Robust Hexahedral Panoramic Video Stitching. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_57
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