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30 April 1992 Toward reverse engineering: accuracy of adaptive thin-plate surface fitting
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This paper presents algorithms to create a robust CAD-compatible surface model of an object utilizing data input from an active ranging device. The algorithm is divided into two stages, each of which approximates the data. The first stage 'cleans' the data and creates a dense grid. The second stage uses uniform B-Splines as the finite element to create a global description of the surface. This technique is a regularized approximation and can deal with data which does not necessarily have to lie on a grid, or be regularly spaced. The first stage of the algorithm eliminates outliers, while the second stage smooths over Gaussian noise. The algorithm can also be used to reconstruct a surface from sparse binocular stereo disparities including errors due to mismatches. The algorithms have been implemented and tested on a wide variety of data: sparse data from binocular stereo, depth measurements from laser radar, and depth measurements from active triangulation using a plane of light. In this paper, we present results of varying the parameters of the second stage of the model, in order to better understand the behavior of the algorithm, and to provide users with quantitative measures of the effect of the variations in order to choose the optimal parameters for a particular application.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Saravajit Sahay Sinha, Brian G. Schunck, and Seth Rogers "Toward reverse engineering: accuracy of adaptive thin-plate surface fitting", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992);


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