APPROXIMATE ORTHOGONAL DISTANCE REGRESSION METHOD FOR FITTING QUADRIC SURFACES TO RANGE DATA

Fitting surfaces to 3-D data is one of the basic methods of surface description for 3-D vision. Most techniques of surface fitting proposed in the literature are ''least-squares''-based that rarely produce satisfactory results if noise level is very high or if the data points are sampled from a small area. A new approach is presented in this paper that minimizes the mean squared approximate orthogonal distances with linearization using Newton's iteration method. This approach usually yields a good fit and the algorithm is reliable and efficient for real applications. Results are reported for synthetic data and several examples of real range data. Experimental results demonstrate that the approximate orthogonal distance performs better than the least squares based methods.