https://en.wikipedia.org/wiki/Toroidal_and_poloidal_coordinates
For example, I see there are the basic rotation matrices for each axis shown in 1 but how would I find that in toroidal coordinates?
I currently have two images of a plane in real life from straight above. One to use as a reference image, and another when the plane has undergone a rotation fixed at the centre of the plane thus changing its orientation. The camera stays at a constant position.
I was wondering if I found the homography matrix of this rotation in opencv and then decomposed the homography matrix in order to find the rotation matrix whether this would yield accurate results and I would be able to find the three angles needed to describe the planes rotation in euclidean coordinates to a reasonable degree of accuracy.
Thanks
I have a projected view of a 3D scene. The 2D points are computed by multiplying the 3D points in homogenous coordinates by a view matrix (which includes a translation and rotation) and a perspective matrix. I want to allow the user to move control points which describe the three axes, and update the rotation matrix based on this.
How do I compute the new rotation matrix given a change in projected 2D coordinates, assuming rotation around the origin? Solving for the position of the end of the single axis has a large degeneracy in the set of possible, but maybe solving for rotation in the axes perpendicular to the moved axis might work.
Given are the vector of direction in which the SCNCamera looks and the up vector that points into the upside direction of the camera.
How can the rotation of the camera of each individual axis be calculated?
I'm using VisualSfM to build the 3D reconstruction of a scene. Now I want to estimate the depthmap and reproject the image. Any idea on how to do it?
If you have the camera intrinsic matrix K, its position vector in the world C and an orientation matrix R that rotates from world space to camera space, you can iterate over all pixels x,y in your image and perform:
Then, find using ray tracing, the minimal t that causes the ray to intersect with your 3D model (assuming it's dense, otherwise interpolate it), so that P lies on your model. The t value you found is then the pixel value of the depth map (perhaps normalized to some range).