Abstract
This paper extends the likelihood kernel density estimate of the visual hull proposed by Kim et al [1] by introducing a prior. Inference of the shape is performed using a meanshift algorithm over a posterior kernel density function that is refined iteratively using both a multiresolution framework (to avoid local maxima) and using KNN for selecting the best reconstruction basis at each iteration. This approach allows us to recover concave areas of the shape that are usually lost when estimating the visual hull.
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Kim, D., Dahyot, R. (2012). Bayesian Shape from Silhouettes. In: Salerno, E., Çetin, A.E., Salvetti, O. (eds) Computational Intelligence for Multimedia Understanding. MUSCLE 2011. Lecture Notes in Computer Science, vol 7252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32436-9_7
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DOI: https://doi.org/10.1007/978-3-642-32436-9_7
Publisher Name: Springer, Berlin, Heidelberg
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