Abstract
We present quantitative results for computing local least squares and global regularized range flow using both image and range data. We first review the computation of local least squares range flow and then show how its computation can be cast in a global Horn and Schunck like regularization framework [15]. These computations are done using both range data only and using a combination of image and range data [14]. We present quantitative results for these two least squares range flow algorithms and for the two regularization range flow algorithms for one synthetic range sequence and one real range sequence, where the correct 3D motions are known a priori. We show that using both image and range data produces more accurate and more dense range flow than the use of range flow alone.
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Barron, J.L., Spies, H. (2001). The Fusion of Image and Range Flow. In: Klette, R., Gimel’farb, G., Huang, T. (eds) Multi-Image Analysis. Lecture Notes in Computer Science, vol 2032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45134-X_13
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DOI: https://doi.org/10.1007/3-540-45134-X_13
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