This is pretty old school, but I recommend Multiple View Geometry by Hartley and Zisserman (http://www.robots.ox.ac.uk/~vgg/hzbook/ http://www.robots.ox.ac.uk/~

This is pretty old school, but I recommend Multiple View Geometry by Hartley and Zisserman (http://www.robots.ox.ac.uk/~vgg/hzbook/ http://www.robots.ox.ac.uk/~vgg/hzbook/) to get through the fundamentals...it's really good to understand the geometric foundations for the past 4 decades. Along the same lines, you have Introductory Techniques for 3-D Computer Vision by Trucco and Verri (https://www.amazon.com/Introductory-Techniques-3-D-Computer-Vision/dp/0132611082 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 (http://szeliski.org/Book/ http://szeliski.org/Book/) had some really amazing coverage of CV in practice. Mastering OpenCV (https://www.amazon.com/Mastering-OpenCV-Daniel-Lelis-Baggio/dp/1786467178 https://www.amazon.com/Mastering-OpenCV-Daniel-Lelis-Baggio/...) was very useful when actually implementing some algorithms.