Multiple View Geometry in Computer Vision

I graduated engineering school several years ago and was a bit intimidated to start something so new, but Hartley does a magnificent job of breaking things down into readable English. Have you ever wondered why 4x4 matrices are used in computer graphics? In school we got a half-way answer about the projective matrix, but it was never really satisfying. This book explains the math behind that and much more clearly, leaving you with many "ah ha" moments.

I value text books that are clearly written. You can look at the description of two books, they seem identical, but one is clear and easy to understand, while the other makes no sense at all. This text book is very clearly written, and it's a pleasure to read. The book is almost entirely self contained, though understanding Projective Geometry first will help. "Geometry and Analysis of Projective Spaces" by C. E. Springer is a good choice for that. The only weak spot in this book was the description of Plucker lines. Fortunately, these were not used extensively later in the text.

This book introduces the detail and necessary knowledge in remodeling 3D from multiple images captured either simultaneously or chronologically. For advanced researcher, this book is extremely helpful but it is quite difficult for beginner's level. For anybody wanting to study this field from scratch, i would recommend "Introductory Techniques for 3-D Computer Vision" which is rather easy and explains the algorithm without the requirement of possessing prior field knowledge.

First a disclaimer. I am not an expert on projective geometry. However, I found the topics to be clearly explained and motivated so that I had no trouble following the progression. I only wish I had found this book sooner, as I spent much time and effort developing algorithms and formulae that I could have picked straight from the chapters of this text.

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.

If you're interested in photogrammetry, buy this book. Extremely well written, extremely informative, and more clear than I could have hoped for. The only thing it doesn't provide is written out code (not even pseudocode) - just a plain-English description of each algorithm's steps.

Great book for anyone interested in Geometry Computer Vision. Can explain in a way you can pick it up and use it. Again it was a gift and makes a wonderful gift for someone in this field.

Sad that the book cover was folded.

Well written, clear and concise. Builds complexity at a sensible level.

I have been doing research in 3D computer vision since 2008, but I really got a chance to buy this book just only recently. This is a great book for researcher who studies 3D computer vision and vision-based robotics (i.e.: reconstructing 3D model from multiple images, camera calibration, using stereo camera for visual odometry, etc.). One thing that I admire from this book is the ability of both authors to explain the philosophy behind projective space and homogeneous coordinate. You can find a bunch of equations on the other books, but you can only find here the reason of why we need to use projective space when dealing with 3D reconstruction from multiple images. Geometry and algebra require strong imagination. However, a picture is worth a thousand words. This book provides several illustrations that help you understand what the authors meant in explaining several crucial terms, such as affine transformation, distortion from camera projection, etc. Several suggestions for better improvement: 1. The companion website is useful: http://www.robots.ox.ac.uk/~vgg/hzbook/ However, it is not periodically updated. If the authors can provide a live blog or a github account to provide contributed source code, this book will be really awesome and useful, even for beginners. I really admire how Matthew A. Russell (author of Mining the Social Web) helps his readers developing their skill using his book. He also gives opportunity to his readers to delve broader topics. Take a look at these pages: https://github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition http://miningthesocialweb.com/ 2. It is better if authors provide "learning road map" for whom newly entering research area in 3D computer vision. New researcher often asks: "What should I do to grasp the content of this book? You said that your book is a primary source in 3D computer vision, but what are introductory references needed to understand your book?" Personally, I will suggest you to read a classic "An Introductory Techniques for 3-D Computer Vision" by Trucco and Verri (1998) before getting "bigger image" of what can be done by 3D computer vision. For math, "Introduction to Linear Algebra" by Strang is my favorite. Finally, I really recommend this book in your reading list, if you are working on 3D computer vision. You will never regret to have this book in your bookshelf.

Este libro es una muy buena referencia sobre geometría proyectiva aplicada a visión por computadora. Es una base sólida para la visión multilocular. Esta literatura abarca tópicos desde los principios de geométrica proyectiva, modelo de cámara pinhole, modelo de sistemas estéreo, rectificación de imagen, tensores triloculares, métodos de reconstrucción tridimensional entre otros. Este libro debe leerse cuidadosamente –detalladamente por no decir--, ya que el autor precisa en muchos detalles para los tópicos, y es fácil perderse si se omite algún de estos. En general, la teoría expuesta en mi experiencia es replicable y experimentable (por ejemplo métodos de triangulación y calibración de dispositivos pinhole) además, este libro contiene algunos métodos matemáticos conocidos sintetizados de manera ecuánime en los apéndices. Sin duda recomendado ampliamente.

Das Buch habe ich als Begleitwerk für eine Vorlesung gekauft. Das Buch ist sehr verständlich geschrieben und hat mir sehr gut geholfen Vieles zu verstehen, was in der Vorlesung nicht sehr ausführlich erklärt wurde. Im Endeffekt hat der Prof aus diesem Buch einfach abgeschrieben. In seinen Folien hat er aber nur die Grundaussagen mit ein paar Stichworten benutzt. Man braucht schon bestimmte Grundkentnisse in Mathematik, um dem Autor folgen zu können, vor allem in der analytischen Geometrie und der linearen Algebra. Solche Themen wie Vektoren, Matrizen, Geraden, Ebenen, lineare Abbildungen müssen bekannt sein. Wenn man diese Themen nicht kann, wird man NICHTS verstehen und sollte das Buch nicht kaufen. Das Buch ist zwar in English, was auf einige (wie mich) zuerst abschreckend wirkt (weil der Inhalt auch nicht gerade trivial ist), die Sprache hat aber eine gewisse Leichtigkeit, der Satzaufbau ist einfach (paar Wörter müsste ich natürlich nachschlagen). Im Prinzip ist das fast das gleiche Vokabular, was man für ein Mathebuch braucht.

Excellent book on the subject.