IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Segmentation of the Apparent Contour for 3D Modeling of Cutting Tools from Single View
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Automatic 3D model reconstruction of cutting tools from a single camera
Computers in Industry
Shape reconstruction and texture sampling by active rectification and virtual view synthesis
Computer Vision and Image Understanding
Camera calibration with two arbitrary coaxial circles
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
3D database population from single views of surfaces of revolution
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A study on automatic on-machine inspection system for 3D modeling and measurement of cutting tools
Journal of Intelligent Manufacturing
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In this paper, we address the problem of the automatic metric reconstruction Surface of Revolution (SOR) from a single uncalibrated view. The apparent contour and the visible portions of the imaged SOR cross sections are extracted and classified. The harmonic homology that models the image projection of the SOR is also estimated. The special care devoted to accuracy and robustness with respect to outliers makes the approach suitable for automatic camera calibration and metric reconstruction from single uncalibrated views of a SOR. Robustness and accuracy are obtained by embedding a graph-based grouping strategy (Euclidean Minimum Spanning Tree) into an Iterative Closest Point framework for projective curve alignment at multiple scales. Classification of SOR curves is achieved through a 2-dof voting scheme based on a pencil of conics novel parametrization. The main contribution of this work is to extend the domain of automatic single view reconstruction from piecewise planar scenes to scenes including curved surfaces, thus allowing to create automatically realistic image models of man-made objects. Experimental results with real images taken from the internet are reported, and the effectiveness and limitations of the approach are discussed.