PCA-based 3D shape reconstruction of human foot using multiple viewpoint cameras
This paper describes a multiple camera-based method to reconstruct the 3D shape of a human foot. From a foot database, an initial 3D model of the foot represented by a cloud of points is built. The shape parameters, which can characterize more than 92% of a foot, are defined by using the principal component analysis method. Then, using “active shape models”, the initial 3D model is adapted to the real foot captured in multiple images by applying some constraints (edge points’ distance and color variance). We insist here on the experiment part where we demonstrate the efficiency of the proposed method on a plastic foot model, and also on real human feet with various shapes. We propose and compare different ways of texturing the foot which is needed for reconstruction. We present an experiment performed on the plastic foot model and on human feet and propose two different ways to improve the final 3D shape’s accuracy according to the previous experiments’ results. The first improvement proposed is the densification of the cloud of points used to represent the initial model and the foot database. The second improvement concerns the projected patterns used to texture the foot. We conclude by showing the obtained results for a human foot with the average computed shape error being only 1.06 mm.
KeywordsShape measurement 3D reconstruction from multiview cameras principal component analysis
Unable to display preview. Download preview PDF.
- M. Kouchi. Japanese Body Dimension Data, [Online], Available: http://www.dh.aist.go.jp/research/centered/anthropometry/NIBHdatabook.html.en, December 6, 2007.
- S. Weik. A Passive Full Body Scanner Using Shape from Silhouettes. In Proceedings of the 15th International Conference on Pattern Recognition, IEEE Press, Barcelona, Spain, pp. 1750–1753, 2000.Google Scholar
- Y. Wang, L. H. Staib. Boundary Finding with Correspondence Using Statistical Shape Models. In Proceedings of IEEE Coference on Computer Vision and Pattern Recognition, IEEE Press, Santa Barbara, CA, USA, pp.338–345, 1998.Google Scholar
- J. Y. Bouguet. Complete Camera Calibration Toolbox for Matlab, [Online], Available: http://www.vision.caltech.edu/bouguetj/calib, December 6, 2007.