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Rigidity checking for matching 3D point correspondence under perspective projection

University of British Columbia

A solution is proposed for the problem of determining the correspondence between sets of point features extracted from a pair of images taken of a static scene from disparate viewpoints. The relative position and orientation between the viewpoints as well as the structure of the scene is assumed to be unknown. Point features from a pair of views are deemed to be in correspondence if they are projectively determined by the same scene points. The determination of correspondences is a critical sub-task for recovering the structure of the world from a set of images taken by a moving camera, a task usually referred to as structure-from-motion, or for determining the relative motion between the scene and the observer. A key property of a static world, assumed by the proposed method, is rigidity. Rigidity of the world and knowledge of the intrinsic camera parameters determines a powerful constraint on point correspondences. The main contribution of this thesis is the rigidity checking method. Rigidity checking is a tractable and robust algorithm for verifying the potential rigidity of a set of hypothesized three-dimensional correspondences from a pair of images under perspective projection. The rigidity checking method, which is based on a set of structure-from-motion constraints, is uniquely designed to answer the question, "Could these corresponding points from two views be the projection of a rigid configuration?" The rigidity constraint proposed in this thesis embodies the recovery of the extrinsic (relative orientation) camera parameters which determine the epipolar geometry - the only available geometric constraint for matching images. The implemented solution combines radiometric and geometric constraints to determine the correct set of correspondences. The radiometric constraint consists of a set of grey-level differential invariants due to Schmid and Mohr. Several enhancements are made to the grey-level differential invariant matching scheme which improves the robustness and speed of the method. The specification of differential invariants for grey-scale images is extended to color images, and experimental results for matching point features with color differential invariants are reported.

Thesis/Dissertation

application/pdf

2009-04-17

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http://hdl.handle.net/2429/7366

Computer Science

University of British Columbia

UBCV

DSpace