ToF depth image motion blur detection using 3D blur shape models

Seungkyu Lee; Hyunjung Shim; James D. K. Kim; Chang Yeong Kim

doi:10.1117/12.908055

10 February 2012 ToF depth image motion blur detection using 3D blur shape models

Seungkyu Lee, Hyunjung Shim, James D. K. Kim, Chang Yeong Kim

Proceedings Volume 8296, Computational Imaging X; 829615 (2012) https://doi.org/10.1117/12.908055
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

Time-of-flight cameras produce 3D geometry enabling faster and easier 3D scene capturing. The depth camera, however, suffers from motion blurs when the movement from either camera or scene appears. Unlike other noises, depth motion blur is hard to eliminate by any general filtering methods and yields the serious distortion in 3D reconstruction, typically causing uneven object boundaries and blurs. In this paper, we provide a through analysis on the ToF depth motion blur and a modeling method which is used to detect a motion blur region from a depth image. We show that the proposed method correctly detects blur regions using the set of all possible motion artifact models.

Citation Download Citation

Seungkyu Lee, Hyunjung Shim, James D. K. Kim, and Chang Yeong Kim "ToF depth image motion blur detection using 3D blur shape models", Proc. SPIE 8296, Computational Imaging X, 829615 (10 February 2012); https://doi.org/10.1117/12.908055

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