I've implemented Marching Cube and Marching Tetrahedron algorithms to convert a voxel grid into a polygon mesh. Now I'm interested in doing the opposite, taking a polygon mesh and approximating it in a voxel grid.
I'm currently just working out my approach and curious if anyone has any guidelines. I can find the list of triangles that intersect any voxel cube fairly easily, but how do you convert the triangles into values held by the voxel vertices?
Steps
Determine which cubes are inside, outside, and on the border. Border is easy to determine, since if the a cube contains any triangles it is on the border.
From there I imagine I need to follow the triangle normals and project along the voxel grid to determine inside/outside. Mark all vertices that are completely surrounded by inside as 1 and all surrounded by outside as -1.
?? This is the part i'm confused about. I need to take the triangles and somehow interpolate their values into vertex values. My guess is I need to find all points of the triangle that collide with the AABB of the voxel subunit or inside it and project it onto all subunit axes. From there I need to take those accumulated positions and figure out what the values should be based by setting the values between [-1,1] such that interpolation would most closely approximate the hull within the boundary unit. <--- this part is what i don't 100% understand.
If I understand correctly, the values you should associate to the voxel corners are signed distances to the surface (positive outside, negative inside), so that the surface itself is at level zero.
If a voxel is cut by a single triangle, you can just assign the distance to the plane of the triangle. If there are several triangles that cross, the situation is more complex. You might consider the orthogonal projections of the corners onto the triangles and see to which triangle they belong.
Related
I have an irregular polygon in 3D, and have coordinates of points present on the boundary of the polygon. Assume that I have placed cubes of some size on all such points present on the boundary. With the help of these cubes I can search for all 3D objects which intersect with my polygon. Now in order search for all objects which are either inside or outside my polygon I need to populate the 3D polygon with such cubes.
Can anyone help on how can I fill in my 3D polygon with all such cubes.
You dont need to fill the polyhedron (this is the name of a 3d polygon), instead you could calculate its hull, which might be a Convex Hull or concave hull depending on your intent.
Newbie to three.js. I have multiple n-sided polygons to be displayed as faces (I want the polygon face to be opaque). Each polygon is facing a different direction in 3D space (essentially theses faces are part of some building).
Here are a couple of methods I tried, but they do not fit the bill:
Used Geometry object and added the n-vertices and used line mesh. It created the polygon as a hollow polygon. As my number of points are not just 3 or 4, I could not use the Face3 or Face4 object. Essentially a Face-n object.
I looked at the WebGL geometric shapes example. The shape object works in 2D and extrusion. All the objects in the example are on one plane. While my requirement is each polygon has a different 3D normal vector. Should I use 2D shape and also take note of the face normal and rotate the 2D shape after rendering.
Or is there a better way to render multiple 3D flat polygons with opaque faces with just x, y, z vertices.
As long as your polygons are convex you can still use the Face3 object. If you take one n-sided polygon, lets say a hexagon, you can create Face3 polygons by taking vertices numbered (0,1,2) as one face, vertices (0,2,3) as another face, vertices (0,3,4) as other face and vertices (0,4,5) as last face. I think you can get the idea if you draw it on paper. But this works only for convex polygons.
I have two 2D rectangles, defined as an origin (x,y) a size (height, width) and an angle of rotation (0-360°). I can guarantee that both rectangles are the same size.
I need to calculate the approximate area of intersection of these two rectangles.
The calculation does not need to be exact, although it can be. I will be comparing the result with other areas of intersection to determine the largest area of intersection in a set of rectangles, so it only needs to be accurate relative to other computations of the same algorithm.
I thought about using the area of the bounding box of the intersected region, but I'm having trouble getting the vertices of the intersected region because of all of the different possible cases:
I'm writing this program in Objective-C in the Cocoa framework, for what it's worth, so if anyone knows any shortcuts using NSBezierPath or something you're welcome to suggest that too.
To supplement the other answers, your problem is an instance of line clipping, a topic heavily studied in computer graphics, and for which there are many algorithms available.
If you rotate your coordinate system so that one rectangle has a horizontal edge, then the problem is exactly line clipping from there on.
You could start at the Wikipedia article on the topic, and investigate from there.
A simple algorithm that will give an approximate answer is sampling.
Divide one of your rectangles up into grids of small squares. For each intersection point, check if that point is inside the other rectangle. The number of points that lie inside the other rectangle will be a fairly good approximation to the area of the overlapping region. Increasing the density of points will increase the accuracy of the calculation, at the cost of performance.
In any case, computing the exact intersection polygon of two convex polygons is an easy task, since any convex polygon can be seen as an intersection of half-planes. "Sequential cutting" does the job.
Choose one rectangle (any) as the cutting rectangle. Iterate through the sides of the cutting rectangle, one by one. Cut the second rectangle by the line that contains the current side of the cutting rectangle and discard everything that lies in the "outer" half-plane.
Once you finish iterating through all cutting sides, what remains of the other rectangle is the result.
You can actually compute the exact area.
Make one polygon out of the two rectangles. See this question (especially this answer), or use the gpc library.
Find the area of this polygon. See here.
The shared area is
area of rectangle 1 + area of rectangle 2 - area of aggregated polygon
Take each line segment of each rectangle and see if they intersect. There will be several possibilities:
If none intersect - shared area is zero - unless all points of one are inside the other. In that case the shared area is the area of the smaller one.
a If two consecutive edges of one rectactangle intersect with a single edge of another rectangle, this forms a triangle. Compute its area.
b. If the edges are not consequtive, this forms a quadrilateral. Compute a line from two opposite corners of the quadrilateral, this makes two triangles. Compute the area of each and sum.
If two edges of one intersect with two edges of another, then you will have a quadrilateral. Compute as in 2b.
If each edge of one intersects with each edge of the other, you will have an octagon. Break it up into triangles ( e.g. draw a ray from one vertex to each other vertex to make 4 triangles )
#edit: I have a more general solution.
Check the special case in 1.
Then start with any intersecting vertex, and follow the edges from there to any other intersection point until you are back to the first intersecting vertex. This forms a convex polygon. draw a ray from the first vertex to each opposite vetex ( e.g. skip the vertex to the left and right. ) This will divide it into a bunch of triangles. compute the area for each and sum.
A brute-force-ish way:
take all points from the set of [corners of
rectangles] + [points of intersection of edges]
remove the points that are not inside or on the edge of both rectangles.
Now You have corners of intersection. Note that the intersection is convex.
sort the remaining points by angle between arbitrary point from the set, arbitrary other point, and the given point.
Now You have the points of intersection in order.
calculate area the usual way (by cross product)
.
What is a fast algorithm for determining whether or not a point is inside a 3D mesh? For simplicity you can assume the mesh is all triangles and has no holes.
What I know so far is that one popular way of determining whether or not a ray has crossed a mesh is to count the number of ray/triangle intersections. It has to be fast because I am using it for a haptic medical simulation. So I cannot test all of the triangles for ray intersection. I need some kind of hashing or tree data structure to store the triangles in to help determine which triangle are relevant.
Also, I know that if I have any arbitrary 2D projection of the vertices, a simple point/triangle intersection test is all necessary. However, I'd still need to know which triangles are relevant and, in addition, which triangles lie in front of a the point and only test those triangles.
I solved my own problem. Basically, I take an arbitrary 2D projection (throw out one of the coordinates), and hash the AABBs (Axis Aligned Bounding Boxes) of the triangles to a 2D array. (A set of 3D cubes as mentioned by titus is overkill, as it only gives you a constant factor speedup.) Use the 2D array and the 2D projection of the point you are testing to get a small set of triangles, which you do a 3D ray/triangle intersection test on (see Intersections of Rays, Segments, Planes and Triangles in 3D) and count the number of triangles the ray intersection where the z-coordinate (the coordinate thrown out) is greater than the z-coordinate of the point. An even number of intersections means it is outside the mesh. An odd number of intersections means it is inside the mesh. This method is not only fast, but very easy to implement (which is exactly what I was looking for).
This is algorithm is efficient only if you have many queries to justify the time for constructing the data structure.
Divide the space into cubes of equal size (we'll figure out the size later). For each cube know which triangles has at least a point in it. Discard the cubes that don't contain anything. Do a ray casting algorithm as presented on wikipedia, but instead o testing if the line intersects each triangle, get all the cubes that intersect with the line, and then do ray casting only with the triangles in these cubes. Watch out not to test the same triangle more than one time because it is present in two cubes.
Finding the proper cube size is tricky, it shouldn't be neither to big or too small. It can only be found by trial and error.
Let's say number of cubes is c and number of triangles is t.
The mean number of triangles in a cube is t/c
k is mean number of cubes that intersect the ray
line-cube intersections + line-triangle intersection in those cubes has to be minimal
c+k*t/c=minimal => c=sqrt(t*k)
You'll have to test out values for the size of the cubes until c=sqrt(t*k) is true
A good starting guess for the size of the cube would be sqrt(mesh width)
To have some perspective, for 1M triangles you'll test on the order of 1k intersections
Ray Triangle Intersection appears to be a good algorithm when it comes to accuracy. The Wiki has some more algorithms. I am linking it here, but you might have seen this already.
Can you, perhaps improvise by, maintaining a matrix of relationship between the points and the plane to which they make the vertices? This subject appears to be a topic of investigation in the academia. Not sure how to access more discussions related to this.
I've got a bunch of overlapping triangles from a 3D model projected into a 2D plane. I need to merge each island of touching triangles into a closed, non-convex polygon.
The resultant polygons shouldn't have any holes in them (since the source data doesn't).
Many of the source triangles share (floating point identical) edges with other triangles in the source data.
What's the easiest way to do this? Performance isn't particularly important, since this will be done at design time.
Try gpc, or the General Polygon Clipper Library.
Imagine the projection onto a plane as a "view" of the model (i.e. the direction of projection is the line of sight, and the projection is what you see). In that case, the borders of the polygons you want to compute correspond to the silhouette of the model.
The silhouette, in turn, is a set of edges in the model. For each edge in the silhouette, the adjacent faces will have normals that either point away from the plane or toward the plane. You can check this be taking the dot product of the face normal with the plane normal -- look for edges whose adjacent face normals have dot products of opposite signs with the projection direction.
Once you have found all the silhouette edges you can join them together into the boundaries of the desired polygons.
Generally, you can find more about silhouette detection and extraction by googling terms like mesh silouette finding detection. Maybe a good place to start is here.
I've also found this[1] approach, which I will be trying next.
[1] 2d outline algorithm for projected 3D mesh