This article presents a fast and accurate method to measure human faces for medical applications. To encode an object point, several random patterns are projected. A correlation technique, which takes only the area of one pixel into account, is used to locate the homologous points. It could be shown that band limited random patterns are helpful for noise reduction. The comparison of the point cloud of a measured plane with an ideal one showed a standard deviation less then 50 mum. Furthermore a depth difference of 20 mum is detectable.

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In this article a rapid and self-calibrating stereophotogrammetry based method for 3D measurements is described. The approach uses a series of statistical generated illumination patterns to encode the surface under test. The allocation of homologous points, which is necessary for 3D reconstruction by triangulation, is done by an adapted correlation technique.

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