3d-reconstruction

问题 I am trying to use the Computer Vision System Toolbox to calibrate the pair of cameras below in order to be able to generate a 3-D point cloud of a vehicle at a range between 1 to 5m. The output image size was approximately 1 MB per image for the checkerboard calibration images and the checkerboard square size was 25 mm. The cameras used were a pair of SJ4000 HD1080P cameras. The cameras were placed as parallel to each other as possible with no angle in the vertical axis. The checkboard

问题 In the hope for a broader audience, I repost my question here which I asked on answers.opencv.org as well. TL;DR : What relation should hold between the arguments passed to undistortPoints , findEssentialMat and recoverPose ? I have code like the following in my program, with K and dist_coefficients being camera intrinsics and imgpts. matching feature points from 2 images. Mat mask; // inlier mask undistortPoints(imgpts1, imgpts1, K, dist_coefficients, noArray(), K); undistortPoints(imgpts2,

问题 I have a calibrated camera whose intrinsics were calculated prior to doing an initial two view reconstruction. Suppose I have 20 images around a static, rigid body all taken with the same camera. Using the first two views and a ground-truth measurement of the scene, I have the 1) initial reconstruction using Stewenius 5 point algorithm to find E (essential matrix). 2) camera matrices P1 and P2 where the origin is set to that of camera P1. My question is, how would I add more views? For the

问题 Specifically I'd ideally want images with point correspondences and a 'Gold Standard' calculated value of F and left and right epipoles. I could work with an Essential matrix and intrinsic and extrinsic camera properties too. I know that I can construct F from two projection matrices and then generate left and right projected point coordinates from 3D actual points and apply Gaussian noise but I'd really like to work with someone else's reference data since I'm trying to test the efficacy of

问题 How would one extract the components of the intrinsic and extrinsic calibration parameters from OpenNI for a device such as the PrimeSense? After some searching I only seem to find how to do it through ROS, but it is not clear how it would be done with just OpenNI. 来源： https://stackoverflow.com/questions/41110791/openni-intrinsic-and-extrinsic-calibration

问题 Say I plan to use OpenCV for 3D reconstruction using a stereo approach...and I do not have any special stereo camera but only webcams. 1.)How do I build a cheap stereo setup using a set of web cams? 2.)Is it possible to snap two images using web cams and convert them to stereo using openCV API? I will use the stereo algorithm from the link below Stereo vision with OpenCV Using this approach I want to create a detailed mapping of an indoor environment. (I would not like to use any projects

问题 I have previously asked the question "Use calibrated camera get matched points for 3D reconstruction", but the problem was not described clearly. So here I use a detail case with every step to show. Hope there is someone can help figure out where my mistake is. At first I made 10 3D points with coordinates: >> X = [0,0,0; -10,0,0; -15,0,0; -13,3,0; 0,6,0; -2,10,0; -13,10,0; 0,13,0; -4,13,0; -8,17,0] these points are on the same plane showing in this picture: My next step is to use the 3D-2D

问题 I am trying to perform a 3D surface reconstruction from a stereo configuration with OpenCV example files. I have created a stereo camera from 2 web cams. I have obtained the calibration parameters using stereo_calib.cpp ( https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/cpp/stereo_calib.cpp?rev=4086 ) and generated a point cloud with stereo_match.cpp ( https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/c/stereo_match.cpp?rev=2614 ). The resulting point cloud, opened

问题 I have successfully calculated Rotation, Translation with the intrinsic camera matrix of two cameras. I also got rectified images from the left and right cameras. Now, I wonder how I calculate the 3D coordinate of a point, just one point in an image. Here, please see the green points. I have a look at the equation, but it requires baseline which I don't know how to calculate. Could you show me the process of calculating the 3d coordinate of the green point with the given information (R, T,

问题 Problem statement I am trying to reproject 2D points to their original 3D coordinates, assuming I know the distance at which each point is. Following the OpenCV documentation, I managed to get it to work with zero-distortions. However, when there are distortions, the result is not correct. Current approach So, the idea is to reverse the following: into the following: By: Geting rid of any distortions using cv::undistortPoints Use intrinsics to get back to the normalized camera coordinates by