Line Based Trinocular Stereo

An approach to solving the stereo correspondence problem in trinocular stereo vision is described. It is based on geometric matching constraints relating the orientation of lines extracted in three images taken from different viewpoints. These novel constraints are termed unary orientation and binary orientation constraints. Matching is achieved within an optimisation framework in which the constraints are encoded into a cost function that is optimised using the simulated annealing method. Results are demonstrated and the characteristics of the approach are explored on both synthetic and real 1 trinocular images.

What can two images tell us about a third one

This paper discusses the problem of predicting image features in an image from image features in two other images and the epipolar geometry between the three images. We adopt the most general camera model of perspective projection and show that a point can be predicted in the third image as a bilinear function of its images in the first two cameras, that the tangents to three corresponding curves are related by a trilinear function, and that the curvature of a curve in the third image is a linear function of the curvatures at the corresponding points in the other two images. Our analysis relies heavily on the use of the fundamental matrix which has been recently introduced and on the properties of a special plane which we call the trifocal plane. We thus answer completely the following question : given two views of an object, what would a third view look like ? the question and its answer bear upon several areas of computer vision, stereo, motion analysis, and model-based object recognition. Our answer is quite general since it assumes the general perspective projection model for image formation and requires only the knowledge of the epipolar geometry for the triple of views. We show that in the special case of orthographic projection our results for points reduce to those of Ullman and Basri. We demonstrate on synthetic as well as on real data the applicability of our theory