What is it about?

In case of lack of sufficient geometric information (homogeneity or isotropicy of curvatures) the surface matching may fail, since there is not a unique solution geometrically, e.g. in case of matching of two planes or spherical objects. An object surface may have some attribute information attached to it. Intensity, color, and temperature are well known examples. Most of the laser scanners can supply intensity information in addition to the Cartesian coordinates for each point, or an additional camera may be used to collect texture. We propose an algorithm that can simultaneously match intensity information and surface geometry under a combined estimation model. In this approach the intensity image of the point cloud also contributes observation equations to the system, considering the intensities as supplementary information to the range image.

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Why is it important?

The technique can be applied to a great variety of data co-registration problems. Since it reveals the sensor noise level and accuracy potential of any kind of surface measurement method or device, it can be used for comparison and validation studies. In addition time dependent (temporal) variations of the object surface can be inspected, tracked, and localized using the statistical analysis tools of the method.

Perspectives

An algorithm for the least squares matching of overlapping 3D surfaces is presented. It estimates the transformation parameters between two or more fully 3D surfaces, using the Generalized Gauss-Markoff model, minimizing the sum of squares of the Euclidean distances between the surfaces. This formulation gives the opportunity of matching arbitrarily oriented 3D surfaces simultaneously, without using explicit tie points.

Dr Devrim AKCA
Isik University

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This page is a summary of: A flexible mathematical model for matching of 3D surfaces and attributes, January 2005, SPIE,
DOI: 10.1117/12.587527.
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