2018-06-18 11:27:05 -0500 received badge ● Popular Question (source) 2016-01-22 07:36:41 -0500 received badge ● Student (source) 2015-10-20 14:20:23 -0500 received badge ● Scholar (source) 2015-09-30 13:37:12 -0500 commented answer solvePnP returning incorrect values Updated the question to include the diagram and added some descriptors (Also credited you at the bottom :) ). The update contains some information that may complicate the process, so if you're willing to give it a go read the latest edit! I tried out your method as you described it and checked the math in Matlab after I obtained all the transforms but it didn't work. This is likely due to the new issue I've discovered though. 2015-09-30 11:40:46 -0500 received badge ● Supporter (source) 2015-09-30 11:40:25 -0500 commented answer solvePnP returning incorrect values The second example you gave is the most accurate one to represent my system, although the second "camera" is actually a stereo-optical tracking system, so I can't acquire any pixel values from it (Which is fine because it returns transforms to the markers anyway!). While I understand the equation you provided, I'm unclear on how to obtain c1Mch as Camera 1's pose is entirely unknown to the tracking system. Is that referring to the transform returned by cv::SolvePnP? If so, does that imply that I have the correct transform from PnP and I'm just manipulating it incorrectly while trying to obtain c1Mm? Your diagram is really spot on, by the way. Can I add it to my question to help others visualize? 2015-09-29 14:33:55 -0500 received badge ● Editor (source) 2015-09-29 13:53:14 -0500 answered a question Real time pose - tutorial Did you calibrate your camera using the intrinsic matrix? If not, you may need to apply it to the frame using cv::undistort() as the lens distortion of the camera causes issues with the registration process. 2015-09-29 13:53:13 -0500 asked a question solvePnP returning incorrect values I'm currently trying to implement an alternate method to webcam-based AR using an external tracking system. I have everything in my environment configured save for the extrinsic calibration. I decided to use cv::solvePnP() as it supposedly does pretty much exactly I want, but after two weeks I am pulling my hair out trying to get it to work. A diagram below shows my configuration. c1 is my camera, c2 is the optical tracker I'm using, M is the tracked marker attached to the camera, and ch is the checkerboard. As it stands I pass in my image pixel coordinates acquired with cv::findChessboardCorners(). The world points are acquired with reference to the tracked marker M affixed to the camera c1 (The extrinsic is thus the transform from this marker's frame to the camera origin). I have tested this with data sets up to 50 points to mitigate the possibility of local minima, but for now I'm testing with only four 2D/3D point pairs. The resulting extrinsic I get from the rvec and tvec returned from cv::solvePnP() is massively off in terms of both translation and rotation relative to both a ground truth extrinsic I manually created as well as a basic visual analysis (The translation implies a 1100mm distance while the camera is at most 10mm away). Initially I thought the issue was that I had some ambiguities in how my board pose was determined, but now I'm fairly certain that's not the case. The math seems pretty straightforward and after all my work on setting the system up, getting caught up on what is essentially a one-liner is a huge frustration. I'm honestly running out of options, so if anyone can help I would be hugely in your debt. My test code is posted below and is the same as my implementation minus some rendering calls. The ground truth extrinsic I have that works with my program is as follows (Basically a pure rotation around one axis and a small translation): 1 0 0 29 0 .77 -.64 32.06 0 .64 .77 -39.86 0 0 0 1  Thanks! #include #include int main() { int imageSize = 4; int markupsSize = 4; std::vector imagePoints; std::vector markupsPoints; double tempImage[3], tempMarkups[3]; // Temp variables or iterative data construction cv::Mat CamMat = (cv::Mat_(3,3) << (566.07469648019332), 0, 318.20416967732666, 0, (565.68051204299513), -254.95231997403764, 0, 0, 1); cv::Mat DistMat = (cv::Mat_(5,1) << -1.1310542849120900e-001, 4.5797249991542077e-001, 7.8356355644908070e-003, 3.7617039978623504e-003, -1.2734302146228518e+000); cv::Mat rvec = cv::Mat::zeros(3,1, cv::DataType::type); cv::Mat tvec = cv::Mat::zeros(3,1,cv::DataType::type); cv::Mat R; cv::Mat extrinsic = cv::Mat::eye(4, 4, CV_64F); // Escape if markup lists aren't equally sized if(imageSize != markupsSize) { //TODO: Replace with try, throw error code, and catch in qSlicerLocalizationModuleWidget return 0; } // Four ...