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Multiple View Geometry in Computer Vision Second Edition .

Richard Hartley .

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Part 0: Background. This part is more tutorial than the others. It introduces the central ideas in the projective geometry of 2-space and 3-space (for example ideal points, and the absolute conic); how this geometry may be represented, manipulated, and estimated; and how the geometry relates to various objectives in computer vision such as rectifying images of planes to remove perspective distortion. .

Part 1: Single view geometry. Here the various cameras that model the perspective projection from 3-space to an image are defined and their anatomy explored. Their estimation using traditional techniques of calibration objects is described, as well as camera calibration from vanishing points and vanishing lines. .

Part 2: Two view geometry. This part describes the epipolar geometry of two cameras, projective reconstruction from image point correspondences, methods of resolving the projective ambiguity, optimal triangulation, transfer between views via planes. .

Part 3: Three view geometry. Here the trifocal geometry of three cameras is described, .

including transfer of a point correspondence from two views to a third, and similarly transfer for a line correspondence; computation of the geometry from point and line correspondences, retrieval of the camera matrices. .

Part 4: N-views. This part has two purposes. First, it extends three view geometry to four views (a minor extension) and describes estimation methods applicable .

to N-views, such as the factorization algorithm of Tomasi and Kanade for computing structure and motion simultaneously from multiple images. Second, .

it covers themes that have been touched on in earlier chapters, but can be understood more fully and uniformly by emphasising their commonality. Examples include deriving multi-linear view constraints on correspondences, auto-calibration, and ambiguous solutions. .

Appendices. These describe further background material on tensors, statistics, parameter .

estimation, linear and matrix algebra, iterative estimation, the solution of sparse matrix systems, and special projective transformations. .

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