Apologies in advance for my feeble maths.
I'm trying to be able to find the corners of a plane in space based on the equation of that plane. Here's what I know. I know three points on the plane and I know where they fall in the 2d coordinate space of the plane (x,y) and where they are in 3d space. I know the width and height of the plane and I can now calculate the equation of the plane. The plane sits on the inside of a large sphere that surrounds the origin so, in theory, it should more or less face where the camera is (though in my diagram it doesn't face the origin as it's just for illustrative purposes)
But it's not clear to me how I can use that to figure out another point. One thought I had was to find the transform that moves the plane parallel to the xy axis and rotate it round one of the points (so it stays in the same place), find the position of the new point, and then rotate it by the inverse of that transform. But it's not clear to me how I would find that transform matrix or how to use it. Could I do this using the normal and vector maths? I understand what normals are, but I'm fuzzy about how to use them.
So, for anyone familiar with Google Maps, when you zoom, it does it around the cursor.
That is to say, the matrix transformation for such a zoom is as simple as:
TST^{-1}*x
Where T is the translation matrix representing the point of focus, S the scale matrix and x is any arbitrary point on the plane.
Now, I want to produce a similar effect with a spherical camera, think sketchfab.
When you zoom in and out, the camera needs to be translated so as to give a similar effect as the 2D zooming in Maps. To be more precise, given a fully composed MVP matrix, there exists a set of parallel planes that are parallel to the camera plane. Among those there exists a unique plane P that also contains the center of the current spherical camera.
Given that plane, there exists a point x, that is the unprojection of the current cursor position onto the camera plane.
If the center of the spherical camera is c then the direction from c to x is d = x - c.
And here's where my challenge comes. Zooming is implemented as just offsetting the camera radially from the center, given a change in zoom Delta, I need to find the translation vector u, colinear with d, that moves the center of the camera towards x, such that I get a similar visual effect as zooming in google maps.
Since I know this is a bit hard to parse I tried to make a diagram:
TL;DR
I want to offset a spherical camera towards the cursor when I zoom, how do i pick my translation vector?
I have a webpage that I am using a Google Map on. When the user drags the map and lets go, I need to query a server for all data points that fall within the bounds of the visible region of the map. I can quite easily get the North-East and South-West coordinate of the visible region of the map through the javascript API, essentially providing a bounding rectangle. However on the server, I am relying on a database whose geographic query API only supports queries in the form of a center point and a radius. So basically what I would like to do is figure out the minimum radius circle I would need to at least encompass the North-East and South-West points.
The simplest algorithm I thought of involved finding the center point between the NE and SW coordinate and then measuring the radius as the distance from the center point to either the NE or SW coordinate. In a simple euclidean space I'd be comfortable doing this, but I think I'd probably get something wrong with the Earth's non-flat coordinate system. I haven't even been able to convince myself that if I knew that centerpoint that the distance would be the same between the center and NE and the center and SW.
I've come across algorithms for smallest circles on a flat 2D surface and also algorithms describing the opposite i.e. bounding box from circle center and radius. I haven't come across a concise algorithm for this particular problem though.
I assume what you call the east-west and north-south coordinates are the longitude and latitude. You can convert them to Cartesian points and find the midpoint between the edge points of your region. This will yield a point C' below Earth's surface with the same latitude and longitude as your centre point C. (This will only work if the difference of your longitudes is smaller than 180°, however; otherwise you'll get a point on the opposite side of the earth, but with the same latitude.) If you need Cartesian coordinates for your centre point, you can project C' onto the surface by adjusting the radius to find your new centre point.
The distance bewteen the two points on the surface of Earth can be calculated with the great-circle disnatce formula.
Transformation is easy if you assume that Earth is a perfect sphere with radius R = 6373 km:
x = R * cos(lat) * cos(lon)
y = R * cos(lat) * sin(lon)
z = R * sin(lat)
and back:
lon = atan2(y, x)
lat = atan2(z, r) with r = sqrt(x*x + y*y)
(But Earth does not have a constant radius, so you might want to use a better coordinate system, maybe ECEF as explained in this answer if you need more precision.)
My first thought was to find your midpoint in terms of longitude and latitude, which should be okay if you take care of wrapping for the latitude. Then you calculate your distance accpording to the great-circle formula. But averaging the longitudes and latitudes does not seem to be sensible if your map region includes a pole.
I have a stack of images (about 180 of them) and there are 2 stars (just basic annotations) on every single image. Hence, the position (x,y) of the two stars are provided initially. The dimensions of all these images are fixed and constant.
The 'distance' between the image is about 1o with the origin to be the center (width/2, height/2) of every single 2D image. Note that, if this is plotted out and interpolated nicely, the stars would actually form a ring of an irregular shape.
The dotted red circle and dotted purple circle are there to give a stronger scent of a 3D space and the arrangement of the 2D images (like a fan). It also indicates that each slice is about 1o apart.
With the provided (x,y) that appeared in the 2D image, how do you get the corresponding (x,y,z) in the 3d space knowing that each image is about 1o apart?
I know that MATLAB had 3D plotting capabilities, how should I go about implementing the solution to the above scenario? (Unfortunately, I have very little experience plotting 3D with MATLAB)
SOLUTION
Based on the accepted answer, I looked up a bit further: spherical coordinate system. Based on the computation of phi, rho and theta, I could reconstruct the ring without problems. Hopefully this helps anyone with similar problems.
I have also documented the solution here. I hope it helps someone out there, too:
http://gray-suit.blogspot.com/2011/07/spherical-coordinate-system.html
I believe the y coordinate stays as is for 3D, so we can treat this as converting 2D x and image angle to an x and z when viewed top down.
The 2D x coordinate is the distance from the origin in 3D space (viewed top down). The image angle is the angle the point makes with respect to the x axis in 3D space (viewed top down). So the x coordinate (distance from orign) and the image angle (angle viewed top down) makes up the x and z coordinates in 3D space (or x and y if viewed top down).
That is a polar coordinate.
Read how to convert from polar to cartesian coordinates to get your 3D x and z coordinates.
I'm not great at maths either, here's my go:
3D coords = (2Dx * cos(imageangle), 2Dy, 2Dx * sin(imageangle))
Given the 2D coordinates (x,y) just add the angle A as a third coordinate: (x,y,A). Then you have 3D.
If you want to have the Anotations move on a circle of radius r in 3D you can just calculate:
you can use (r*cos(phi),r*sin(phi),0) which draws a circle in the XY-plane and rotate it with a 3x3 rotation matrix into the orientation you need.
It is not clear from you question around which axis your rotation is taking place. However, my answer holds for a general rotation axis.
First, put your points in a 3D space, lying on the X-Y plane. This means the points have a 0 z-coordinate. Then, apply a 3D rotation of the desired angle around the desired axis - in your example, it is a one degree rotation. You could calculate the transformation matrix yourself (should not be too hard, google "3D rotation matrix" or similar keywords). However, MATLAB makes it easier, using the viewmtx function, which gives you a 4x4 rotational matrix. The extra (fourth) dimension is dependent on the projection you specify (it acts like a scaling coefficient), but in order to make things simple, I will let MATLAB use its default projection - you can read about it in MATLAB documentation.
So, to make the plot clearer, I assume four points which are the vertices of a square lying on the x-y plane (A(1,1), B(1,-1), C(-1,-1), D(1,-1)).
az = 0; % Angle (degrees) of rotation around the z axis, measured from -y axis.
el = 90; % Angle (degrees) of rotation around the y' axis (the ' indicates axes after the first rotation).
x = [1,-1, -1, 1,1]; y = [1, 1, -1, -1,1]; z = [0,0, 0, 0,0]; % A square lying on the X-Y plane.
[m,n] = size(x);
x4d = [x(:),y(:),z(:),ones(m*n,1)]'; % The 4D version of the points.
figure
for el = 90 : -1 :0 % Start from 90 for viewing directly above the X-Y plane.
T = viewmtx(az, el);
x2d = T * x4d; % Rotated version of points.
plot3 (x2d(1,:), x2d(2,:),x2d(3,:),'-*'); % Plot the rotated points in 3D space.
grid
xlim ([-2,2]);
ylim ([-2,2]);
zlim([-2,2]);
pause(0.1)
end
If you can describe your observation of a real physical system (like a binary star system) with a model, you can use particle filters.
Those filters were developed to locate a ship on the sea, when only one observation direction was available. One tracks the ship and estimates where it is and how fast it moves, the longer one follows, the better the estimates become.
So we have such situation:
In this illustration, the first quadrilateral is shown on the Image Plane and the second quadrilateral is shown on the World Plane. [1]
In my particular case the Image Plane has 3 quadrilaterals - projections of real world squares, which, as we know, have same size, lying on the same plane, with same rotation relative to the plane they are lying on, and are not situated on same line on plane.
I wonder if we can get rotation angles of Image Plane to World Plane knowing stuff described?
In my case as input I have such data structures: original image (RGB pixels), objects (squares) with angles points in pixels (x,y) on Image Plane.
Take a look at Sections 2 and 3 of Algorithms for plane-based pose estimation.
The methods described there assume that you know the (x,y) coordinates of the features in question - in this case the red squares.
The problem you are describing is generally known as pose estimation - determining the 3D orientation and position of an object relative to a camera from a 2D view. For you, the object is a plane. Googling 'pose estimation plane' should give you more sources.