Least-squares 3D reconstruction from one or more views and geometric clues

Authors:
Etienne Grossmann;José Santos-Victor
Affiliations:
UK Center for Visualization and Virtual Environments, 1 Quality St, Lexington, KY 40507, USA;Instituto Superior Técnico, Instituto de Sistemas e Robótica, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Venue:
Computer Vision and Image Understanding
Year:
2005

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Abstract

We present a method to reconstruct from one or more images a scene that is rich in planes, alignments, symmetries, orthogonalities, and other forms of geometrical regularity. Given image points of interest and some geometric information, the method recovers least-squares estimates of the 3D points, camera position(s), orientation(s), and eventually calibration(s). Our contributions lie (i) in a novel way of exploiting some types of symmetry and of geometric regularity, (ii) in treating indifferently one or more images, (iii) in a geometric test that indicates whether the input data uniquely defines a reconstruction, and (iv) a parameterization method for collections of 3D points subject to geometric constraints. Moreover, the reconstruction algorithm lends itself to sensitivity analysis. The method is benchmarked on synthetic data and its effectiveness is shown on real-world data.