Multiple View Geometry in Computer Vision

There was a failure in the delivery, the mailman gave the package to someone else. I have no way to be refund by post Canada. I did order the book once again and i received it.

This book is very complete and rigorous in its explanations of the theory. However, I just think I like the approach in An Invitation to 3-D Vision a bit better. This book is better illustrated than that one and is more careful in its explanations, but this book just seems more focused on providing complete proofs than giving you a feel for how you would approach a real problem. Even the exercises are more along the lines of proofs. I like how An Invitation to 3-D Vision ends the book with a complete example. In all fairness, though, this book does have quite a bit of Matlab code on its website.

The book begins with some background material on 2D and 3D geometry. Then the author explains single-view geometry and how cameras map an image in 3D space to an image. Two-view geometry is next, with the author describing the epipolar geometry of two cameras ahd projective reconstruction from resulting image map correspondences. Part three of the book extends ideas to three cameras and the resulting trifocal geometry. The final section of the book takes the algorithms of the book to N views. Thus this book has a simple and straightforward structure that belies the complexity of the material.

If you are really researching this subject you should probably have this book for explanation, illustrations, and rigor, and the Invitation book for enlightenment through a good example-based approach. You should also have Introductory Techniques for 3-D Computer Vision as a text on the individual pieces of algorithms involved in 3D vision. And don't even think about getting into this subject unless you already have a firm foundation in linear algebra, image processing, and computer vision in general as found in Computer Vision, which is my favorite introductory computer vision text.

I received the fourth printing a few weeks ago. It is missing pages 177-208. That includes all of chapter seven, on camera calibration. Ridiculous.

I find the book very useful, it is full of practically useful content. Formulas, theorems, lots of examples and illustrations. Overall very easy to read and understand, though requires you to recall your forgotten mathematical skills. The book does present what it claims on the first pages, so read the abstract and judge for yourself if you need the book. For my purposes, I found it to contain all the material I needed to perform certain image photo transformations and compositions. There is also lots of reference material, in terms definitions, formulas and theorems with proofs. And it's good to have it all in one place.

Overall I would say it is worth the money.

The book has a lot of valuable information for those who are working in computer vision. The book however is fairly terse on many subject and requires careful reading.