Abstract
Standard 3D ranging and imaging systems process only a single return from an assumed single opaque surface. However, there are situations when the laser return consists of multiple peaks due to the footprint of the beam impinging on a target with surfaces distributed in depth or with semi-transparent surfaces. If all these returns are processed, a more informative multi-layered 3D image is created. We propose a unified theory of pixel processing for ladar data using a Bayesian approach that incorporates spatial constraints through a Markov Random Field. The different parameters of the several returns are estimated using reversible jump Markov chain Monte Carlo (RJMCMC) techniques in combination with an adaptive strategy of delayed rejection to improve the estimates of the parameters.
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Hernandez-Marin, S., Wallace, A.M., Gibson, G.J. (2006). Creating Multi-layered 3D Images Using Reversible Jump MCMC Algorithms. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_42
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DOI: https://doi.org/10.1007/11919629_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48626-8
Online ISBN: 978-3-540-48627-5
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