I'm not really sure if this fits in here or better in a scientific computer science or math forum but since I'm searching for a concrete algorithm...
I have a 3d model which is somehow defined either by a mesh or as an algebraic variety and i want to remesh/approximate this thing just using a fixed chosen type of congruent tiles, e.g. isoscele triangles with certain ratio of sides length to the base length. Is there a algorithm for that or does anyone know the right name for the problem? I found some algorithms that come close to what I need, but they all mesh via some tolerance in the length and different sizes of the tiles.
In freeform shapes tiling is achieved via a very complicated algorithm. In real world architecture there is this method of tiling with as many identical tiles as possible and still get the shape, but there are angle tolerances and all sort of tolerances that you can manipulate. check paneling of freeform shapes.
Related
So a polygon mesh is defined as the following:
class Triangle{
int vertices[3]; //vertex indices
float nx, ny, nz; //face-plane normal
};
Is this a convenient way to represent a mesh used with flat shading? Explain
Suggest an object for which this is a good mesh format when used with Gouraud shading. Explain
Suggest an object for which this is a bad mesh format when used with Gouraud shading. Explain
So for 1, I said yes because the face plane normal can be easily converted to a point in the middle of the face. I read somewhere that normals don't have positions?
For 2 I said a ball; more gentle angles
And 3 a box; steeper angles.
I don't know, I don't think I really understand what the normal vector is.
mostly yes
from geometry computations is this OK however from rendering aspect having triangles in indices form only can be sometimes problematic (depends on the rendering engine, HW, etc). Usually is faster to have the triangle points directly in vector form instead of just indexes sometimes triangle contains both... However that is wasting space.
depends on how you classify what is OK and what not.
smooth objects like sphere will look like this
while flat side meshes like cube will be rendered without visible distortions in shape (but with flat shaded like colors only so lighting will be corrupted)
So answer to this is depend on what you want to achieve less lighting error, or better shape recognition or what. Basically using 1 normal for face will turn Gourard into flat shading.
Lighting can be improved by dividing big flat surfaces into more triangles
is unanswerable exactly for the same reasons as #2
So if you want to answer #2,#3 you need to clarify what it means good and bad ...
I am sketching out a new simulation that will involve thousands of ships moving around on Earth's oceans and interacting over long periods of time. So, lots of "intersection detection" for sensor and communications ranges, as well as region detection for various environmental conditions. We'll assume a spherical earth, not WGS84. This is an event-step simulation that spits out metrics, not a real time game or anything like that.
A question is to use Cartesian coordinates (Earth-Centered, Earth-Fixed) or Geodic/polar coordinates. With polar coordinates a ship's track would be internally represented as a series of lat/lon waypoints with times and a great circle paths between them. With a Cartesian representation the waypoints would be connected with polyline renderings of the great circle between them.
The reason this is a question is I suspect that by sticking to a Cartesian data model it becomes possible to use various geometry libraries that are performance tuned, and even offer up SIMD/GPU performance advantages. The polar coordinates would probably be the more natural way to proceed if writing everything from scratch. But I suspect that by keeping things Cartesian I will have greater access to better and faster libraries. Is this an invalid line of thought? Another consideration is that I know polar coordinate calculations tend to get really screwy when near the poles.
Just curious if somebody with experience could save me a whole lot of time prototyping some scenarios both ways.
It often works well to represent directions as unit vectors instead of angles. Rotation of a vector by another angle becomes a 2x2 or 3x3 matmul (efficient with SIMD, but still more expensive than an FP add of two numbers in radians), but you very rarely need sin/cos.
You may occasionally want atan2 to get an angle, but usually not inside tight loops.
Intersection-detection can be very efficient (with SIMD) for XYZ coordinates given another XYZ + range. I'm not sure how efficiently you could check which lat/lon pairs were within range of a given point, not a problem I've looked at.
IDK what kind of stuff you'd find in existing libraries, or what you'd want to do with it.
I am looking for an algorithm that given two meshes could clip one using another.
The simplest form of this is clipping a mesh using a plane. I've already implemented that by following something similar to what is described here.
What it does is basically inspecting all mesh vertices and triangles with respect to the plane (the plane's normal and point are given). If the triangle is completely above the plane, it is left untouched. If it falls completely below the plane, it is discarded. If some of the edges of the triangle intersect with the plane, the intersecting points with the plane are calculated and added as the new vertices. Finally a cap is generated for the hole on the place the mesh was cut.
The problem is that the algorithm assumes that the plane is unlimited, therefore whatever is in its path is clipped. In the simplest form, I need an extension of this without the assumption of a plane of "infinite" size.
To clarify, imagine that we have a 3D model of a desk with 2 boxes on it. The boxes are adjacent (but not touching or stacked). The user will define a cutting plane of a limited width and height underneath the first box and performs the cut. We end up with a desk model (mesh) with a box on it and another box (mesh) that can be freely moved around/manipulated.
In the general form, I'd like the user to be able to define a bounding box for the box he/she wants to separate from the desk model and perform the cut using that bounding box.
If I could extend the algorithm I already have to an algorithm with limited-sized planes, that would be great for now.
What you're looking for are constructive solid geometry/boolean algorithms with arbitrary meshes. It's considerably more complex than slicing meshes by an infinite plane.
Among the earliest and simplest research in this area, and a good starting point, is Constructive Solid Geometry for Polyhedral Objects by Trumbore and Hughes.
http://cs.brown.edu/~jfh/papers/Laidlaw-CSG-1986/main.htm
From the original paper:
More elaborate solutions extend upon this subject with a variety of data structures.
The real complexity of the operation lies in the slicing algorithm to slice one triangle against another. The nightmare of implementing robust CSG lies in numerical precision. It's easy when you involve objects far more complex than a cube to run into cases where a slice is made just barely next to a vertex (at which point you have the tough decision of merging the new split vertex or not prior to carrying out more splits), where polygons are coplanar (or almost), etc.
So I suggest initially erring on the side of using very high-precision floating point numbers, possibly even higher than double precision to focus on getting something working correctly and robustly. You can optimize later (first pass should be to use an accelerator like an octree/kd-tree/bvh), but you'll avoid many headaches this way in your first iteration.
This is vastly simpler to implement at render time if you're focusing on a raytracer rather than a modeling software, e.g. With raytracers, all you have to do to do this kind of arbitrary clipping is pretend that an object used to subtract from another has its polygons flipped in the culling process, e.g. It's easy to solve robustly at the ray level, but quite a bit harder to do robustly at the geometric level.
Another thing you can do to make your life so much easier if you can afford it is to voxelize your object, find subtractions/additions/unions of voxels, and then translate the voxels back into a mesh. This is so much easier to make robust, but harder to do efficiently and the voxel->polygon conversion can get quite involved if you want better results than what marching cubes provide.
It's a really tough area to do extremely well and requires perseverance, and thus the reason for the existence of things like this: http://carve-csg.com/about.
If someone is interested, currently there is a solution for this problem in CGAL library. It allows clipping one triangular mesh using another mesh as bounding volume. The usage example can be found here.
I have got point clouds of different primitive objects (cone, plane, torus, cylinder, sphere, ellipsoid). The all vary in orientation, position and scaling. Furthermore all of them are initialized with a unique set of parameters (e.g. height, radius, etc.) so that their shape can be quiet different (some cones are tall, others are small and fat).
Now to my question:
I am trying to find the objects "principal components". Using PCA doesn't lead to good results, since rotated primitives can have their main variation in any direction (which doesn't have to be necessarily along the length of the objects).
The only chance that I see is to use somehow the symmetry of my primitives. Isn't there a method based on inertia? Maybe some way to find the main symmetry axis and two others perpendicular to it?
Can you give me some advice or point me to papers or implementations (maybe even python)?
Thanks a lot, Merlin.
PS: This is what I get if I only apply a PCA. Especially for cones this doesn't really work. Only cones that are almost identical in shape share the same orientation, but I need them all to point in one direction (e.g. up).
So you got cones and just need to rotate them all in the same direction?
If so you can fit a triangle to them and point the peak (e.g with the perpendicular bisectors of the sides) to your main axis.
You have an interesting problem. Normally used shape descriptors (VFH) that are invariant to shape but not pose (which is what you would want, really) would not be invariant to stretching in the shape.
I think to succeed at this you need to be clearer about the invariants that you are trying to maintain when a shape changes. Is it a topological invariant? If so, then here is a good starting point: https://www.google.com.tr/search?q=topologically+invariant+shape+descriptor
I decided to just stick to simple PCA since it's the only method that is totally generic and doesn't depend on prior (expert) knowledge about the data.
I would like to do some odd geometric/odd shape recognition. But I'm not sure how to do it.
Here's what I have so far:
Convert RGB image to Monochrome.
Otsu Threshold
Hough Transform.
I'm not sure what to do next.
For geometric information, you could do a raster to vector conversion to convert your image into coordinated vectors (lines and points) and finite element analysis to look for known shapes. Not easy but libraries should be available for both.
Edit: Note that there are sometimes easier practical solutions, but they depend on the image and types of errors. For example, removing perspective, identifying a 3d object from a 2d image, significance of colour, etc... You often see registration markers added to the real world object to overcome
this and allow much easier identification. Looking up articles on feature extraction techniques might help.