After searching in vain for solutions to the exercises in this book, I decided to start documenting my solutions with the hope that it might provide encouragement to another like me on the path of self-study.

On the note of self-study, I would like to provide some feedback to those who have just begun or are contemplating using this book to aid in their learning of Computer Vision. If you’re already committed to the book, carry on my friend. If not, read on.

For one, this book is certainly not a conventional *textbook*, like for
instance, Gilbert Strang’s book for linear algebra or Thomas’ Calculus book
for, well, Calculus. It is a compendium of research papers on multiple view
geometry for computer vision. It is written to be read by a fellow researcher,
not a bumbling beginner who doesn’t yet know what multiple view geometry even
means. If you’re like me (starting from scratch), this might not be the first
book to consider in your journey. Although the back cover claims quiet
enticingly that you only need to be *familiar* (not even conversant) with
linear algebra and basic numerical methods to understand and implement
algorithms from the book, I say bosh. If you’re not comfortable with projective
geometry, probability & statistics, linear algebra, calculus, optimization and
some image processing like the back of your hand, you will mostly find the
content to be gobbledygook. Now you might say, “Aha! I caught you my friend.
You didn’t understand the text because you skipped the appendix.” I assure you
I most certainly did not. The appendices try to summarize, in a handful of
pages, a whole blessed book on the subject. I don’t know about you but that’s
just not my style.

The second and last note I will leave you with is that multiple view geometry is just one facet of computer vision. As a field, CV is vast. You might want to first explore the field breadth-wise before you decide to commit to one particular field like multiple view geometry or image processing or what have you.