Kim, Donghoon and Ruttle, Jonathan and Dahyot, Rozenn (2013) Bayesian 3D shape from silhouettes. Digital Signal Processing, 23. pp. 1844-1855. ISSN 1051-2004

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Abstract

This paper introduces a smooth posterior density function for inferring shapes from silhouettes. Both the likelihood and the prior are modelled using kernel density functions and optimisation is performed using gradient ascent algorithms. Adding a prior allows for the recovery of concave areas of the shape that are usually lost when estimating the visual hull. This framework is also extended to use colour information when it is available in addition to the silhouettes. In these cases, the modelling not only allows for the shape to be recovered but also its colour information. Our new algorithms are assessed by reconstructing 2D shapes from 1D silhouettes and 3D faces from 2D silhouettes. Experimental results show that using the prior can assist in reconstructing concave areas and also illustrate the benefits of using colour information even when only small numbers of silhouettes are available.

Item Type:	Article
Keywords:	3D reconstruction from multiple view images; Shape-from-silhouettes; Kernel density estimates; K-nearest neighbours; Principal component analysis;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	15118
Identification Number:	https://doi.org/10.1016/j.dsp.2013.06.007
Depositing User:	Rozenn Dahyot
Date Deposited:	14 Dec 2021 15:00
Journal or Publication Title:	Digital Signal Processing
Publisher:	Elsevier
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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