projective-geometry

问题 With the algorithm below, when the projection plane is tangent to the equator (the center line of the equirectangular image), projected image looks rectilinear. But when the projection plane is tilted, (py0 != panorama.height/2), lines are warped. The two last "lines" in the algorithm below needs to be "rectified", in order to adjust px and/or py when the center line of the destination plane is not at the same level than the center line of the equirectangular image. // u,v,w : // Normalized

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 Given a 2d picture of a rectangle distorted by perspective: I know that the shape was originally a rectangle, but I do not know its original size. If I know the pixel coordinates of the corners in this picture, how can I calculate the original proportions, i.e. the quotient ( width / height ) of the rectangle? (background: the goal is to automatically undistort photos of rectangular documents, edge detection will probably be done with hough transform) UPDATE: There has been some discussion on

问题 So the problem is that I have a 3D projection of a rectangle that I want to turn into 2D. That is I have a photo of a sheet of paper laying on a table which I want to transform into a 2D view of that sheet. So what I need is to get an undistorted 2D image by reverting all the 3D/projection transformations and getting a plain view of the sheet from the top. I happened to find some directions on the subject but they don't suggest an immediate instruction on how this can be achieved. It would be

问题 I want to compute a normal map N(i,j) from a depth map D(i,j) , where (i,j) describes 2D pixel location. A basic case of this problem is discussed here: Calculate surface normals from depth image using neighboring pixels cross product In my case, however, D(i,j) is projective depth: A 3D object (given by a set of 3D points) is projected onto a 2D image using an intrinsic camera matrix [K|0] (see below for a definition) by multiplying each 3D point in homogeneous coordinates (i.e., a 4D vector

问题 I have a rotation-translation matrix [R T] (3x4). Is there a function in opencv that performs the rotation-translation described by [R T]? 回答1: A lot of solutions to this question I think make hidden assumptions. I will try to give you a quick summary of how I think about this problem (I have had to think about it a lot in the past). Warping between two images is a 2 dimensional process accomplished by a 3x3 matrix called a homography. What you have is a 3x4 matrix which defines a transform

问题 Suppose I have the coordinates X, Y, Z and orientation Rx, Ry, Rz of an object with respect to a camera. In addition, I have the coordinates U, V, W and orientation Ru, Rv, Rw of this camera in the world. How do I transform the position (location and rotation) of the object to its position in the world? It sounds like a change of basis to me, but I haven't found a clear source yet. 回答1: In fact you have two basis : one relative to the camera, the other is absolute (the world). So you

问题 We have a grid with red squares on it. Meaning we have an array of 3 squares (with angles == 90 deg) which as we know have same size, lying on the same plane and with same rotation relative to the plane they are lying on, and are not situated on same line on plane. We have a projection of the space which contains the plane with squares. We want to turn our plane projection with squares so that we would see it like it's facing us, in general we need a formula for turning each point of that

问题 I want to apply homography to the satellite images. I found this post quite helpful. So I decided to use the same Matlab code. im = imread('cameraman.tif'); n = [0;0;-1]; d = Inf theta = 60*pi/180; R = [ 1 0 0 ; 0 cos(theta) -sin(theta); 0 sin(theta) cos(theta)]; t = [0;0;0]; K=[300 0 0; 0 300 0; 0 0 1]; H=K*R/K-1/d*K*t*n'*K; img=imagehomog(im,H','c'); figure;imshow(img) but the output is just the small box. I am using MATLAB 2015b EDIT Homography using imtransform and maketform n = [0;0;-1];

问题 I use two projection matrices P1 and P2 (for example I'm using dinosaur dataset) and I need to compute the fundamental matrix F. So I use two Matlab functions: Peter Kovesi's function: www.csse.uwa.edu.au/~pk/Research/MatlabFns/Projective/fundfromcameras.m Zisserman: www.robots.ox.ac.uk/~vgg/hzbook/code/vgg_multiview/vgg_F_from_P.m These functions should do the same thing, but I have a different F value ! How it's possible? Which is the right functions? If two points X1 and X2 are "the same"