Introductory Techniques for 3-D Computer Vision

The book Mastering OpenCV helped me a bunch several years ago during implementation. The best advice I can give along with this book is to try and understand the fundamental geometric problem that computer vision tries to solve. It's been the same for like 30-50 years...we've just thrown some ML at it for these past 10-20. For that, I recommend a copy of Trucco and Veri's book that you can probably find floating around online (link below). Good luck!

https://www.amazon.com/Introductory-Techniques-3-D-Computer-...

Trucco and Verri's [Introductory techniques for 3D Computer Vision](https://smile.amazon.com/Introductory-Techniques-3-D-Computer-Vision/dp/0132611082?sa-no-redirect=1) has a section on this. [Here's the relevant pages](https://imgur.com/a/j5HMr). Might be helpful.

This is pretty old school, but I recommend Multiple View Geometry by Hartley and Zisserman (https://www.amazon.com/Introductory-Techniques-3-D-Computer-...), which also goes over the geometry and the fundamental problems that computer vision algorithms try to solve. It often does come down to just applying simple geometry; getting good enough data to run that model is challenging.

If you just throw everything into a neural network, then you won't really understand the breadth of the problems you're solving, and you'll be therefore ignorant of the limitations of your hammer. While NNs are incredibly useful, I think a deep understanding of the core problems is essential to know how to use NNs effectively in a particular domain.

After getting a grip on those concepts, Szeliski's Computer Vision: Algorithms and Applications (https://www.amazon.com/Mastering-OpenCV-Daniel-Lelis-Baggio/...) was very useful when actually implementing some algorithms.