Generating 3D (x,y,z) Point Clouds from Two 2D Stereo Images

Question

I've been trying to read through the Stackoverflow questions for generating point clouds (x,y,z) coordinates from a left and right stereo image pair.

I haven't come to any definite solution, and I'm asking the community here for some help.

Problem statement: Given two stereo images, generate 3D (x,y,z) cartesian coordinate point clouds from those and do so in a way that lends itself to completing this point-cloud generation in a way that could work over a large set (thousands) of pairs of stereo images

My programming language experience lends itself to MATLAB, but I've dabbled in Python, and C++ is limited, but I may be able to work in that as well.

Speed is a factor here, so the the idea is to find a quick method of successively going through these pairs and generating the point cloud.

NOTE: I am not asking for the BEST as to avoid comparative solutions, I'm just asking for solutions.

Thank you very much!

Edit: After being recommended to utilize the Stereo Block Matching implementation in OpenCV, I may steer away from this method, as there is a wide variation in regards to texture of the topographical pictures that I'm dealing with.

Answer 1

What you probably want is called "Stereo Block Matching". If you don't feel like writing it yourself, OpenCV has an implementation ready to go (even a CUDA accelerated version ).