Estimation of General Curves and Surfaces to Edge and Range Data by Euclidean Fitting

The ability to construct CAD or other object models from edge and range data has a fundamental meaning in building a recognition and positioning system. While the problem of model fitting has been successfully addressed, the problem of efficient high accuracy and stability of the fitting is still an open problem. This paper addresses the problem of estimation of general curves and surfaces to edge and range data by a constrained Euclidean fitting. We study and compare the performance of various fitting algorithms in terms of efficiency, correctness, robustness and pose invariance, and present our results improving the known fitting methods by an (iterative) estimation of the real Euclidean distance.