Single View Stereovision

This post details the process of Single View Stereo, a technique replicating stereo vision effects using one camera moved between two positions, overcoming the challenge of unknown relative rotation (R) and translation (T). It outlines finding corresponding feature points between two images to calculate the Fundamental matrix (F) using methods like RANSAC. From F and camera intrinsics, the Essential matrix (E) is derived. E is decomposed via SVD to yield multiple potential R and T solutions. Point triangulation helps select the correct R and T by ensuring reconstructed 3D points lie in front of the camera. Finally, images are rectified, and algorithms like StereoBM generate a disparity map, which is then converted to a depth map. After migration to a new CMS, I couldn't migrate this post, which is why here's a pdf from the old post.