Skip to Main Content
Learning OpenCV
book

Learning OpenCV

by Gary Bradski, Adrian Kaehler
September 2008
Beginner to intermediate content levelBeginner to intermediate
580 pages
20h 7m
English
O'Reilly Media, Inc.
Content preview from 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 ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Learning OpenCV 3

Learning OpenCV 3

Adrian Kaehler, Gary Bradski
Learning OpenCV, 2nd Edition

Learning OpenCV, 2nd Edition

Adrian Kaehler, Gary Bradski
Practical OpenCV

Practical OpenCV

Samarth Brahmbhatt
Machine Learning for OpenCV

Machine Learning for OpenCV

Michael Beyeler, Michael Beyeler (USD)

Publisher Resources

ISBN: 9780596516130Supplemental ContentErrata Page