Say I had a point cloud with n number of points in 3d space(relatively densely packed together). What is the most efficient way to create a surface that goes contains every single point in it and lets me calculate values such as the normal and curvature at some point on the surface that was created? I also need to be able to create this surface as fast as possible(a few milliseconds hopefully working with python) and it can be assumed that n < 1000.
There is no "most efficient and effective" way (this is true of any problem in any domain).
In the first place, the surface you have in mind is not mathematically defined uniquely.
A possible approach is by means of the so-called Alpha-shapes, implemented either from a Delaunay tetrahedrization, or by the ball-pivoting method. For other methods, lookup "mesh reconstruction" or "surface reconstruction".
On another hand, normals and curvature can be computed locally, from neighbors configurations, without reconstructing a surface (though there is an ambiguity on the orientation of the normals).
I could suggest Nina Amenta's Power Crust algorithm (link to code), or also meshlab suite, which can compute the curvatures too.
Related
For one of my classes, I made a 3D graphing application (using Visual Basic). It takes in a string (z=f(x,y)) as input, parses it into RPN notation, then evaluates and graphs the equation. While it did work, it took about 20 seconds to graph. I would have liked to add slide bars to rotate the graph vertically and horizontally, but it was definitely too slow to allow that.
Does anyone know what programming languages would be best for this type of thing? Ideally, I will be able to smoothly rotate the function once it is graphed.
Also, I’m trying to find a better way to rotate the function. Right now, I evaluate it at a bunch of points, and then plot the points to the screen. Every time it is rotated, it must be re-evaluated and plot all the new points. This takes just as long as the original graph process, as it basically treats it as a completely new function.
Lastly, I need a better way to display the graph. Currently (using VB with visual studio) I plot 200,000 points to a chart, but this does not look great by any means. Eventually, I would like to be able to change color based on height, and other graphics manipulation to make it look better.
To be clear, I am not asking for someone to do any of this for me, but rather the means to go about coding this in an efficient way. I will greatly appreciate any advice anyone can give to help with any of these three concerns.
So I will explain how I would go about it using C++ and OpenGL. This doesn't mean those are the tools that you must use, it's just those are standard graphics tools.
Your function's surface is essentially a 2D manifold, which has the nice property of having an intuitive mapping to a 2D space. What is commonly referred to as UV mapping.
What you should do is pick the ranges for the rectangle domain you want to display (minimum x, maximum x, minimum y, maximum y) And make 2 nested for loops of the form:
// Pseudocode
for (x=minimum; x<maximum; x++)
for (y=minimum; y=maximum; y++)
3D point = (x,y, f(x,y))
Store all of these points into a container (std vector for c++ works fine) and this will be your "mesh".
This is done once, prior to rendering. You then render those points using, for example GL_POINTS, and rotate your graph mesh using rotations on the GPU.
This will only show scattered points, not a surface.
If you also wish to show the surface of your function, and not just the points, you can triangulate that set of points fairly easily.
Group each 4 contiguous vertices (i.e the vertices at indices <x,y>, <x+1,y>, <x+1,y>, <x+1,y+1>) and create the 2 triangles:
(<x,y>, <x+1,y>, <x,y+1>), (<x+1,y>, <x+1,y+1>, <x,y+1>)
This will fill triangulate the surface of your mesh.
Essentially you only need to build your mesh once, and this way rendering should be 60 fps for something with 20 000 vertices, regardless of whether you only render points or triangles too.
Programming language is mostly not relevant, so VB itself is probably not the issue. You can have the same issues in Python, C#, C++, etc. Of course you must master the programming language you choose.
One key aspect is using the right algorithms and data-structures. Proper use of memory allocations and memory layout for maximizing CPU (and GPU) cache are also key. Then you must take advantage of the platform and hardware capabilities (GPU and Multithreading). For the last point you definetely need to use a graphics library such as OpenGL or Vulkan.
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'd like to write a program that lets me arbitrarily distort a textured polygon by dragging its vertices. I want the texture to distort fluidly and without overlap, assuming the new polygon doesn't intersect itself. I should also be able to repeat the process with the new shape, and with a minimum amount of loss.
Are there any algorithms for doing this?
It sounds like you might want a variation on the Schwarz-Christoffel mapping. This is a type of conformal mapping that can be used to warp a polygon to and from a simpler region, like a disk; although I have not implemented it, apparently it is computationally tractable.
For your application, you would set up a map from the original polygon to the simpler region, and compute the inverse map to the modified polygon; combining the two should give you a nice conformal mapping from the original to the modified polygon.
Conformal mappings are nice and smooth, but they can sometimes behave in unintuitive ways; I can imagine that an animated version might yield some entertaining "slidy" effects. The conformal mapping will preserve local angles in the interior of the polygon; this means that the size distortion very near a modified vertex can be severe.
People have been working on solutions to this problem for the past decade or two, and the state of the art keeps on getting better and better (but the math gets harder as well). A good place to start (and sort of where I stopped following it) is the work http://www.cs.technion.ac.il/~weber/Publications/Complex-Coordinates/
Read the paper there, and look up the papers in the references. One of them should give you an algorithm that you're willing to implement.
The simplest method I can think of is to triangulate the input polygon (using an ear clipping method, or something similarly good) and then move the points. Then you can use a barycentric mapping from the original polygon to the new space.
If you're looking for something more robust, you might look at mean value coordinates.