Learning OpenCV

POSIT: 3D Pose Estimation

Before moving on to stereo vision, we should visit a useful algorithm that can estimate the positions of known objects in three dimensions. POSIT (aka "Pose from Orthography and Scaling with Iteration") is an algorithm originally proposed in 1992 for computing the pose (the position T and orientation R described by six parameters [DeMenthon92]) of a 3D object whose exact dimensions are known. To compute this pose, we must find on the image the corresponding locations of at least four non-coplanar points on the surface of that object. The first part of the algorithm, pose from orthography and scaling (POS), assumes that the points on the object are all at effectively the same depth^[194] and that size variations from the original model are due solely to scaling with distance from the camera. In this case there is a closed-form solution for that object's 3D pose based on scaling. The assumption that the object points are all at the same depth effectively means that the object is far enough away from the camera that we can neglect any internal depth differences within the object; this assumption is known as the weak-perspective approximation.

Given that we know the camera intrinsics, we can find the perspective scaling of our known object and thus compute its approximate pose. This computation will not be very accurate, but we can then project where our four observed points would go if the true 3D object were at the pose we calculated through POS. We then start ...