Ray and segment of polygon lie in the same plane. The normal vector of this plane is known. I need to know if a ray intersects this segment
Choose the largest component of plane normal and make projection onto corresponding plane OXY, OXZ or OYZ (in general we can use any non-zero component)
Say Z-component is the largest, so make projection onto OXY plane. This projection is very simple - just use X and Y components. You have rather simple 2d problem now. (Example for line segments intersection).
I've got the following problem:
In 3D there's a vector from fixed the center of a plane to the origin. This plane has arbitrary coordinates around this center thus its normal vector is not necessarily the mentioned vector. Therefore I have to rotate the plane around this fixed center such that the mentioned vector is the plane's normal vector.
My first idea was to compute the angle between the vector and the normal vector, but the problem then is how to rotate the plane.
Any ideas?
A plane is a mathematical entity which satisfies the following equation:
Where n is the normal, and a is any point on the plane (in this case the center point as above). It makes no sense to "rotate" this equation - if you want the plane to face a certain direction, just make the normal equal to that direction (i.e. the "mentioned" vector).
You later mentioned in the comments that the "plane" is an OpenGL quad, in which case you can use Quaternions to compute the rotation.
This Stackoverflow post tells you how to compute the rotation quaternion from your current normal vector to the "mentioned" vector. This site tells you how to convert a quaternion into a rotation matrix (whose dimensions are 3x3).
Let's suppose the center point is called q, and that the rotation matrix you obtain has the following form:
This can only rotate geometry about the origin. A rotation about a general point requires a 4x4 matrix (what OpenGL uses), which can be constructed as follows:
Hi I'm trying to do the Ex 6.5 from Szeliski's book ... But I'm stuck at points 3 and 4, I have the theory of what a vanishing point is, but what does it mean to find it for each face? and how about the focal lenght and rotation angle for those VP? If you can provide some resources easy to understand I will appreciate it.
Thanks!
Vanishing points are points at which parallel lines in a plane approach the same point. So in order to find the vanishing points you would take lines from the plane that are parallel (there are 6 of these per face on a Rubik's cube, 3 in 2 different directions) then where those lines intersect in the image is where a vanishing point is for that plane. You should be able to find 2 vanishing points per face unless you're looking at the cube head on.
You are bit mistaken in saying " the focal length and rotation angle for those VP", the book wants you to find these FROM the vanishing points. After you've found these vanishing points using the other points from the face you can construct a plane (in the coordinate system it would be a single vector normal to the plane). The rotation angle for this would be the difference between the image plane and the plane of the face.
Unfortunately I'm not well versed in finding focal lengths. But there should be a way of determining focal length of the camera by knowing the actual distance between the cubes. You could try reading this: http://www.cs.umd.edu/class/spring2013/cmsc426/lectures/camera-calibration.pdf from an image processing class I took.
Here is another geometric problem:
I have created an 3-dimensional triangulated iso-surface of a point cloud using the marching cubes algorithm. Then I intersect this iso-surface with a plane and get a number of line segments that represent the contour lines of the intersection.
Is there any possibility to sort the vertices of these line segments clockwise so that I can draw them as a closed path and do a flood fill?
Thanks in advance!
It depends on how complex your isosurface is, but the simplest thing I can think of that might work is:
For each point, project to the plane. This will give you a set of points in 2d.
Make sure these are centered, via a translation to the centroid or center of the bounding box.
For each 2d point, run atan2 and get an angle. atan2 just puts things in the correct quadrant.
Order by that angle
If your isosurface/plane is monotonically increasing in angle around the centroid, then this will work fine. If not, then you might need to find the 2 nearest neighbors to each point in the plane, and hope that that makes a simple loop. In face, the simple loop idea might be simpler, because you don't need to project and you don't need to compute angles - just do everything in 3d.
How can I create minimal OOBB for given points? Creating AABB or sphere is very easy, but I have problems creating minimal OOBB.
[edit]
First answer didn't get me good results. I don't have huge cloud of points. I have little amount of points. I am doing collision geometry generation. For example, cube has 36 points (6 sides, 2 triangles each, 3 points for each triangle). And algorithm from first post gave bad results for cube. Example points for cube: http://nopaste.dk/download/3382 (should return identity axis)
The PCA/covariance/eigenvector method essentially finds the axes of an ellipsoid that approximates the vertices of your object. It should work for random objects, but will give bad results for symmetric objects like the cube. That's because the approximating ellipsoid for a cube is a sphere, and a sphere does not have well defined axes. So you're not getting the standard axes that you expect.
Perhaps if you know in advance that an object is, for example, a cube you can use a specialized method, and use PCA for everything else.
On the other hand, if you want to compute the true OBB there are existing implementations you can use e.g. http://www.geometrictools.com/LibMathematics/Containment/Containment.html
(archived at https://web.archive.org/web/20110817024344/geometrictools.com/LibMathematics/Containment/Containment.html and https://github.com/timprepscius/GeometricTools/blob/master/WildMagic5/LibMathematics/Containment/Wm5ContMinBox3.cpp). I believe this implements the algorithm alluded to in the comments to your question.
Quoting from that page:
The ContMinBox3 files implement an
algorithm for computing the
minimum-volume box containing the
points. This method computes the
convex hull of the points, a convex
polyhedron. The minimum-volume box
either has a face coincident with a
face of the convex polyhedron or has
axis directions given by three
mutually perpendicular edges of the
convex polyhedron. Each face of the
convex polyhedron is processed by
projecting the polyhedron to the plane
of the face, computing the
minimum-area rectangle containing the
projections, and computing the
minimum-length interval containing the
projections onto the perpendicular of
the face. The minimum-area rectangle
and minimum-length interval combine to
form a candidate box. Then all triples
of edges of the convex polyhedron are
processed. If any triple has mutually
perpendicular edges, the smallest box
with axes in the directions of the
edges is computed. Of all these boxes,
the one with the smallest volume is
the minimum-volume box containing the
original point set.
If, as you say, your objects do not have a large number of vertices, the running time should be acceptable.
In a discussion at http://www.gamedev.net/topic/320675-how-to-create-oriented-bounding-box/ the author of the above library casts some more light on the topic:
Gottschalk's approach to OBB construction is to compute a covariance matrix for the point set. The eigenvectors of this matrix are the OBB axes. The average of the points is the OBB center. The OBB is not guaranteed to have the minimum volume of all containing boxes. An OBB tree is built by recursively splitting the triangle mesh whose vertices are the point set. A couple of heuristics are mentioned for the splitting.
The minimum volume box (MVB) containing a point set is the minimum volume box containing the convex hull of the points. The hull is a convex polyhedron. Based on a result of Joe O'Rourke, the MVB is supported by a face of the polyhedron or by three perpendicular edges of the polyhedron. "Supported by a face" means that the MVB has a face coincident with a polyhedron face. "Supported by three perpendicular edges" means that three perpendicular edges of the MVB are coincident with edges of the polyhedron.
As jyk indicates, the implementations of any of these algorithms is not trivial. However, never let that discourage you from trying :) An AABB can be a good fit, but it can also be a very bad fit. Consider a "thin" cylinder with end points at (0,0,0) and (1,1,1) [imagine the cylinder is the line segment connecting the points]. The AABB is 0 <= x <= 1, 0 <= y <= 1, and 0 <= z <= 1, with a volume of 1. The MVB has center (1,1,1)/2, an axis (1,1,1)/sqrt(3), and an extent for this axis of sqrt(3)/2. It also has two additional axes perpendicular to the first axis, but the extents are 0. The volume of this box is 0. If you give the line segment a little thickness, the MVB becomes slightly larger, but still has a volume much smaller than that of the AABB.
Which type of box you choose should depend on your own application's data.
Implementations of all of this are at my www.geometrictools.com website. I use the median-split heuristic for the bounding-volume trees. The MVB construction requires a convex hull finder in 2D, a convex hull finder in 3D, and a method for computing the minimum area box containing a set of planar points--I use the rotating caliper method for this.
First you have to compute the centroid of the points, in pseudcode
mu = sum(0..N, x[i]) / N
then you have to compute the covariance matrix
C = sum(0..N, mult(x[i]-mu, transpose(x[i]-mu)));
Note that the mult performs an (3x1) matrix multiplication by (1x3) matrix multiplication, and the result is a 3x3 matrix.
The eigenvectors of the C matrix define the three axis of the OBB.
There is a new library ApproxMVBB in C++ online which computes an approximation for the minimum volume bounding box. Its released under MPL 2.0 Licences, and written by me.
If you have time look at: http://gabyx.github.io/ApproxMVBB/
The library is C++11 compatible and only needs Eigen http://eigen.tuxfamily.org.
Tests show that an approximation for 140Million points in 3D can be computed in reasonable time (arround 5-7 seconds) depending on your settings for the approximation.