Abstract
We propose a new robust focal length estimation method in multi-view structure from motion from unordered data sets, e.g. downloaded from the Flickr database, where jpeg-exif headers are often incorrect or missing. The method is based on a combination of RANSAC with weighted kernel voting and can use any algorithm for estimating epipolar geometry and unknown focal lengths. We demonstrate by experiments with synthetic and real data that the method produces reliable focal length estimates which are better than estimates obtained using RANSAC or kernel voting alone and which are in most real situations very close to the ground truth. An important feature of this method is the ability to detect image pairs close to critical configurations or the cases when the focal length can’t be reliably estimated.
This work has been supported by EC project FP7-SPACE-218814 PRoVisG and by Czech Government under the research program MSM6840770038.
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Bujnak, M., Kukelova, Z., Pajdla, T. (2010). Robust Focal Length Estimation by Voting in Multi-view Scene Reconstruction. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_2
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DOI: https://doi.org/10.1007/978-3-642-12307-8_2
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