stereo-3d

I wish to convert a MATLAB stereoParameters structure to intrinsics and extrinsics matrices to use in OpenCV's stereoRectify. If I understood http://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html and http://mathworks.com/help/vision/ref/stereoparameters-class.html , stereoParameters.CameraParameters1 and stereoParameters.CameraParameters2 store the intrinsic matrices and the other members of stereoParameters the extrinsic ones. I think I got this mapping Intrinsics: cameraMatrix1 = stereoParameters.CameraParameters1.IntrinsicMatrix' cameraMatrix2 =

I'm trying to write some code that will calculate the fundamental matrix to determine the relationship between stereo images. I started with the Hartley and Zisserman book that most people recommend, but it didn't have any practical examples and the sample code for it was in MATLAB which I don't have. I then switched to An introduction to 3D Computer Vision Techniques and Algorithms which is more practical and has actual examples in it. I implemented the recommended 8-point algorithm using Python and numpy, but I'm having trouble verifying the validity of it. I'm using the dataset listed on

I'm trying to get a depth map from an uncalibrated method. I can obtain the fundamental matrix via different correspondent points from SIFT method and cv2.findFundamentalMat . Then with cv2.stereoRectifyUncalibrated I can get the rectification matrix. Finally I can use cv2.warpPerspective to rectify and compute the disparity but this latter doesn't conduct to a good depth map. The values are very high so I'm wondering if I have to use warpPerspective or I have to calculate rotation matrix from homography matrix got with stereoRectifyUncalibrated . So I'm not sure of the projective matrix with

I am doing a research in stereo vision and I am interested in accuracy of depth estimation in this question. It depends of several factors like: Proper stereo calibration (rotation, translation and distortion extraction), image resolution, camera and lens quality (the less distortion, proper color capturing), matching features between two images. Let's say we have a no low-cost cameras and lenses (no cheap webcams etc). My question is, what is the accuracy of depth estimation we can achieve in this field? Anyone knows a real stereo vision system that works with some accuracy? Can we achieve 1

I'm planning to calculate disparity map by taking two pictures from the two back cameras of Evo 3D. However, I'm able to use only one camera. I tried different index. index 0 gives me left camera (one of the back cameras) 1 gives me front camera -1 gives me left camera (one of the back cameras). I once got other camera using -1 index, but it's not working anymore. I'm using CameraBridgeViewBase . I have seen on Google group of android-opencv that people have successfully used both cameras of Evo 3D phone. I want to know how to do it? Is there some other index? or is there some other way using

I am trying to implement the (relatively simple) linear homogeneous (DLT) 3D triangulation method from Hartley & Zisserman's "Multiple View Geometry" (sec 12.2), with the aim of implementing their full, "optimal algorithm" in the future. Right now, based on this question , I'm trying to get it to work in Matlab, and will later port it into C++ and OpenCV, testing for conformity along the way. The problem is that I'm unsure how to use the data I have. I have calibrated my stereo rig, and obtained the two intrinsic camera matrices, two vectors of distortion coefficients, the rotation matrix and