In section 25.8.1 Basic Polygon Rasterization of the Vulkan spec it says:
Barycentric coordinates are a set of three numbers, a, b, and c, each in the range [0,1], with a + b + c
= 1. These coordinates uniquely specify any point p within the triangle or on the triangle’s
boundary as
p = a * p_a + b * p_b + c * p_c
where p_a , p_b , and p_c are the vertices of the triangle. a, b, and c are determined by:
a = A(p, p_b, p_c) / A(p_a, p_b, p_c)
b = A(p, p_a, p_c) / A(p_a, p_b, p_c)
c = A(p, p_a, p_b) / A(p_a, p_b, p_c)
where A(l,m,n) denotes the area in framebuffer coordinates of the triangle with vertices l, m, and n.
Framebuffer coordinates technically have three components. This is specified in 24.5 Controlling the Viewport as:
The vertex’s framebuffer coordinates (x_f , y_f , z_f ) are given by [snip]
What precisely is the formula of the A function?
Is it either:
(a) the same as the formula given to calculate whether the triangle is back-facing or front-facing in 25.8.1, namely:
a = -0.5 * sum_i(x_f[i] * y_f[i+1] - x_f[i+1] * y_f[i])
That is, is it taken as read that the forumla of A does not use the z_f components of its arguments, and is purely a function of the (x_f, y_f) components? (ie It is calculating the area of the two dimensional projection of the triangle onto the x-y plane in framebuffer-space)
or (b), does A use all three framebuffer components? ie Does A return the area of the triangle in the full three-dimensional framebuffer-space (like shown here for example)
or (c) something else?
It actually doesn't matter, mathematically speaking. Whichever function you pick, you'll find that the particulars of the math divide out when computing the barycentric coordinate.
A barycentric coordinate is computed by taking the ratio of two areas. If you linearly project two co-planar triangles from 3D space to 2D space with the same projection, the ratio of their areas is unchanged (assuming that they have an area post-projection).
Related
I had attached also the schematic to depict my question.
I need to rotate the vector V with the base point P by an angle and find the new vector V'.
The rotation axis is say for is about a local y axis at point P (which is parallel to global Y axis)
Subsequently, I need to rotate the initial vector V about x axis which is parallel to global Y axis.
The main reason for the rotation is to find the new vector V' at point P. Both the rotations are independent and each of the rotation provides a new V'. I'm programming this in VB.net and output is a double() of new vector V'.
Just apply the two rotations independently (see Wikipedia). The base point does not play any role in this because it is just a constant offset that never changes. If I got your description right, you want the following:
//rotation about y-axis
iAfterRot1 = cos(phi1) * i + sin(phi1) * k
jAfterRot1 = j
kAfterRot1 = -sin(phi1) * i + cos(phi) * k
//rotation about x-axis
iAfterRot2 = iAfterRot1
jAfterRot2 = cos(phi2) * jAfterRot1 - sin(phi2) * kAfterRot1
kAfterRot2 = sin(phi2) * jAfterRot1 + cos(phi2) * kAfterRot1
I'm a little confused about the difference between the DLT algorithm described here and the homography estimation described here. In both of these techniques, we are trying to solve for the entries of a 3x3 matrix by using at least 4 point correspondences. In both methods, we set up a system where we have a "measurement" matrix and we use SVD to solve for the vector of elements that make up H. I was wondering why there are two techniques that seem to do the same thing, and why one might be used over the other.
You have left-right image correspondences {p_i} <-> {p'_i}, where p_i = (x_i, y_i), etc.
Normalizing them to the unit square means computing two shifts m=(mx, my), m'=(mx', my'), and two scales s=(sx,sy), s'=(sx',sy') such that q_i = (p_i - m) / s and q_i' = (p_i' - m') / s', and both the {q_i} and the {q'_i} transformed image points are centered at (0,0) and approximately contained within a square of unit side length. A little math shows that a good choice for the m terms are the averages of the x,y coordinates in each set of image points, and for the s terms you use the standard deviations (or twice the standard deviation) times 1/sqrt(2).
You can express this normalizing transformation in matrix form: q = T p,
where T = [[1/sx, 0, -mx/sx], [0, 1/sy, -my/sy], [0, 0, 1]], and likewise q' = T' p'.
You then compute the homography K between the {q_i} and {q'_i} points: q_i' = K q_i.
Finally, you denormalize K into the origina (un-normalized) coordinates thus: H = inv(T') K T, and H is the desired homography that maps {p} into {p'}.
I have two planes in 3D space as shown below.
Point "e" on plane2 represents the intersection of the line which passes from point "P" of plane1 and has the direction vector of "S". Let P be the edge of plane 1.
Which are the "e" point coordinates (xe,ye, 0) with respect to the coordinates system of the plane it belongs (plane2), using Numpy?
I have the following data available:
Coordinates of the centers of each plane with respect of the global coordinate system "C".
x = np.array([x1, x2])
y = np.array([y1, y2])
z = np.array([z1, z2])
Sun direction vector S = np.array([Sz, Sx, Sy])
Point "P" location with respect to the coordinate system of plane1: P(xp,yp,0)
Each plane has the same width and length dimensions: Hw, Hl
Unit vectors normal to the plane surfaces
n = np.array([[n1z, n1x, n1y], [n2z, n2x, n2y]])
Also the azimuthial and elevation angles for both planes with respect to the global coordinate system "c" are known:
alphaH = np.array([alphaH1, alphaH2])
aH = np.array([aH1, aH2])
You have the position vector for c2 and the position vector for e in the global coordinate system then all you need to do is calculate c2-e and this will give you the position vector of e relative to c2.
I have a straight line in space with an start and end point (x,y,z) and I am attempting to get the angle between this vector and the plane defined by z=0. I am using VB.NET
Here is a picture of the line in my 3d environment (the line I'm intersted in is circled in red) :
It is set to an angle of 70 degrees right now.
You need 2 rays to define an angle.
If you want the angle between a vector and a plane, it is defined for any vector in that plane. However, there is only one minimal value for that, which is the angle between a vector and its projection onto said plane.
Therefore, that minimal value is the one we take when we speak of the angle between a vector and a plane.
This value is also π/2 - the angle between your vector and the the vector that is normal to the plane.You can read more about it all on this site.
With v your vector (thus v.x = end.x - start.x and idem for y and z), n the normal to the plane and a the angle you are looking for, we know from the definition of a scalar product that:
<v,n> = ||v|| * ||n|| * cos(π/2 - a)
We know cos(π/2 - a) = sin(a), and the normal to the z=0 plane is simply the vector n = (0, 0, 1). Thus both the scalar product, v.x * n.x + v.y * n.y + v.z * n.z, and the norm of n, ||n|| = 1, can be simplified a lot. We get the following expression:
sin(a) = v.z / ||v||
Thus finally, the formula by taking the reciprocical of the sine and expliciting the norm of v:
a = Asin(v.z / sqrt( v.x*v.x + v.y*v.y + v.z*v.z ))
According to this documentation the Asin function exists in your System.Math class. It does, however, return the value in radians:
Return Value
Type: System.Double
An angle, θ, measured in radians, such that -π/2 ≤ θ ≤ π/2
-or-
NaN if d < -1 or d > 1 or d equals NaN.
Luckily the same System.Math class contains the value of π so that you can do the conversion:
a *= 180 / Math.PI
I need to find a point or points on the given circle (or curve) which minimizes d0+d1? the radius and center of the curve are (0,0) and 'r' respectively and the coordinates of points A and B are known. Let say A=(x1,y1) and B=(x1,-y1) and r> sqrt(x1^2+y1^2) . C is unknown point of the circle which should minimize the length d0+d1
d0 - the distance between A to C on the circle
d1- the distance between B to C on the circle
point C moves along the circle. I need to find a point or points on the given circle (or curve) which minimizes d0+d1?
If the line AB intersects the circle, then C is that intersection point (note that there can be two intersection points and both give an equal distance d0+d1 !).
If AB does not intersect the circle, then C is the point on the circle intersecting an imaginary line from the point on the line AB closest to the circle center.
There are many articles online about how to find the point on a line closest to another point, and how to find the intersection between two lines, which would solve the second case. For the first case you can google "line circle intersection"
The general case is very complicated, but the special situation
A=(x1,y1) and B=(x1,-y1) and r > sqrt(x1^2+y1^2)
with a circle whose centre is the origin has enough symmetries to make the solution at least in some circumstances accessible. I'm assuming A ≠ B, (equivalently y1 ≠ 0), otherwise the problem is trivial for a circle.
Let dist(P,Q) be the Euclidean distance between the points P and Q. The (closed) line segment connecting A and B is the locus of points P with
dist(P,A) + dist(P,B) = dist(A,B)
For D > dist(A,B), the locus of points with
f(P) = dist(P,A) + dist(P,B) = D
is an ellipse E(D) whose foci are A and B. Let P be a point on the circle and D = f(P).
If the tangents to the circle and to the ellipse E(D) in the point P don't coincide, P is neither a local minimum nor a local maximum of f restricted to the circle.
If the tangents coincide, and the curvature of the circle is larger than the curvature of E(D) in P, then P is an isolated local maximum of f restricted to the circle.
If the tangents coincide, and the curvature of the circle is smaller than the curvature of E(D) in P, then P is an isolated local minimum of f restricted to the circle.
If the tangents coincide and the curvature of the circle is equal to the curvature of E(D) in P, then
P is an isolated local minimum of f restricted to the circle if dist(P,A) = dist(P,B),
P is neither a local maximum nor a local minimum of f restricted to the circle otherwise.
First, if x1 = 0, it is easily seen (in case it is not geometrically obvious) that the points on the circle minimising f are the points with x-coordinate 0, i.e. P1 = (0,r) and P2 = (0,-r). [That would even be true if r² ≤ x1² + y1².]
Now, suppose x1 ≠ 0, without loss of generality x1 > 0. Then it is obvious that a point P = (x,y) on the circle minimising f must have x > x1. By the symmetry of the situation, the point R = (r,0) must either be a local minimum or a local maximum of f restricted to the circle.
Computing the behaviour of f near R, one finds that R is a local minimum if and only if
r ≥ (x1² + y1²) / x1
Since R is a point of smallest curvature of E(f(R)) (and the tangents in R to E(f(R)) and the circle coincide), R is then also the global minimum.
If r < (x1² + y1²) / x1, then R is a local maximum of f restricted to the circle. Then f has two global minima on the circle, with the same x-coordinate. Unfortunately, I don't have a nice formula to compute them, so I can't offer a better way than an iterative search.