Abstract
Depth from defocus (DFD) is a 3D recovery method based on estimating the amount of defocus induced by finite lens apertures. Given two images with different camera settings, the problem is to measure the resulting differences in defocus across the image, and to estimate a depth based on these blur differences. Most methods assume that the scene depth map is locally smooth, and this leads to inaccurate depth estimates near discontinuities. In this paper, we propose a novel DFD method that avoids smoothing over discontinuities by iteratively modifying an elliptical image region over which defocus is estimated. Our method can be used to complement any depth from defocus method based on spatial domain measurements. In particular, this method improves the DFD accuracy near discontinuities in depth or surface orientation.
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McCloskey, S., Langer, M., Siddiqi, K. (2007). Evolving Measurement Regions for Depth from Defocus. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_84
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DOI: https://doi.org/10.1007/978-3-540-76390-1_84
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