Simple SceneKit program in either Xcode Playground or iPad Playground. Place ball(s) having dynamic physics body on a static/kinematic floor. Set world gravity to zero vector. Start program and the ball(s) slowly lift off the surface then hover slightly above it!
How can I find out the new positions of the ball(s) after this intriguing error/feature?
The explanation is simple but interesting. I had positioned the ball centres not a radius above the table but, in error, a half radius. The table surface is a material static surface that will have some default elasticity. With zero gravity the balls still possessed potential energy by being pressed into the table material. When the program was started the physics engine converted this spring energy into a force that kicked the balls above the table surface. Good physics!
Related
For a project I am working on I am cresting a tower and placing it on the floor and enabling gravity.
First I create the floor with a static physics body, then generate a tower of many blocks and place them on the floor. I place them into the scenes root node by getting the position of the anchor node and enable gravity.
The problem is, as the plane moves slightly, the tower becomes very unstable and sometimes falls. I understand this is an issue in ARKit as it constantly refined the scenes positioning, however I just need a static floor so the blocks have a stable platform (it doesn’t have to even be seen).
Is there a way to solve this? Or any hacky solution (making the blocks stick to the floor, or turning off gravity before the scene moves, setting gravity on the platform, etc.?)
I am coding a modern OpenGL application to visualize 3d atomic models (molecules, periodic systems ...) for chemistry and condense matter physics.
I started to work on this few years ago, the first version of my program was in old OpenGL now I am updating it to modern OpenGL.
I come with a question regarding the quality of the rendering of the OpenGL window. In the following examples I draw 3D cylinders and 3D spheres using instanced drawing, in this model to render the bonds I only draw one cylinder, then I translate/scale/rotate it properly in the vertex shader
to render all bonds, same goes for the sphere to render the atoms.
As you can see it works just fine, and the efficiency of the method is amazing and I can render models with hundreds of thousand of atoms smoothly.
However I noticed something weird, that somehow the quality of the rendering seems to be dependent on the number of vertices (objects, atoms and bonds) in the scene, obviously the number of triangles is the most important parameter but not the only one ... since the quality decrease when a lot of vertices are rendered ... please see the attached snapshots:
To render the spheres in the scene I am using 50x50 vertices, and 2x50 for the cylinders (GL_TRIANGLE_STRIP in both cases)
1) In this test model I load: 96 atoms, 512 half bonds, : ~ 291200 vertices:
2) I zoom in to focus on one selected atom and it surrounding, at this scale the result is impeccable:
3) I reset the view and use the builder in my program to increase the number of boxes
(I am simply doing replicas in the 3 direction of space) here I choose to do 20x20x20 replicas,
see the result bellow, the original box is highlighted.
In that scene there are 768000 atoms, 4096000 half-bonds, and thus: 291200x20x20x20 = 2329600000 vertices
quite a lot, yet it works, but something weird appears ...
4) I zoom in again on that particular area of the model I picked before and there is a decrease in quality in particular
in the areas where 3D objects (spheres/cylinders) superimpose/overlap ...
Can somebody explain to me what I see ?
Note 1: In the same window I can decrease the number of replicas back to the original box, zoom again
and see that the result is back to impeccable.
Note 2: the older version of my program still works fine (old OpenGL, using display list with glutsphere and glutcylinders),
I can do the same things, the rendering will take much much longer, but at the end of the process when I zoom in on the 20x20x20
boxes model, the results remains perfect, like for the single box model, and obviously I use same graphic card, driver and else.
Can somebody explain to me what I see ?
You're seeing the limited precision of the depth buffer. There are only so many bits you can work with and in a perspective projection a lonlinear scaling from Z distance to depth buffer value is applied.
The best course of action is to limit the near/depth range of the perspective projection matrix to what's going to be actually visible on screen, to make better use of the depth buffer. Also it's possible to linearize the depth buffer (but that comes with a performance hit). Also you could try to cleanly intersect the geometry where sticks and spheres meet, i.e. constrain the sphere's vertices to the cylinder surface where the sticks and similarly constrain the sticks' end vertices to the sphere where they meet. That way you avoid overlap and hence these artifacts.
I want to analyze a traffic scene. My source data is a point cloud like this one (see images at the bottom of that post). I want to be able to detect objects that are on the road (cars, cyclists etc.). So first of all I need know where the road surface is so that I can remove or ignore these points or simply just run a detection above the surface level.
What are the ways to detect such road surface? The easiest scenario is a straight and flat road - I guess I could try to registrate a simple plane to the approximate position of the surface (I quite surely know it begins just in front of the car) and because the road surface is not a perfect plane I have to allow some tolerance around the plane.
More difficult scenario would be a curvy and wavy (undulated?) road surface that would form some kind of a 3D curve... I will appreciate any inputs.
A relatively simple starting point:
If you can assume that the road surface starts directly in front of the camera then you can use a region growing algorithm to find a region such that the curvature does not change so much within the region (thereby using sharp edges to delineate the region). This would involve calculating the curvature first. This can make a first approximation; there will be issues with occluding objects and other artefacts I am sure.
http://pointclouds.org/documentation/tutorials/region_growing_segmentation.php#region-growing-segmentation
http://pointclouds.org/documentation/tutorials/normal_estimation.php
Suppose we have an image (pixel buffer) that is in black and white, so each pixel is either black or white (not gray scale).
Now somewhere in the middle of that images, place a green dot. It may have a radius of n for rendering purposed, but it is really a just point. Give the dot a randomly selected direction and speed, and start it moving. If the image is all white pixels, the dot will bounce off the edges of the image, infinitely wandering around the picture. This is quite easy... just reverse either the rise or run of the dot's vector.
Next, suppose the image has some globs of black pixels. As the dot encounters these globs of black pixels, the angle of reflection needs to be calculated. This is also quite easy of the the black pixels have a fixed slope, as in my sketch (blue X represents black pixels). You can find the slope of the blue Xs and easily calculate the new vector.
But how about the case where the black pixels form really unfriendly surfaces? What are some approaches to figuring out this angle?
This is the subject that I am interested in.
There must be some algorithms that exist for this kind of purpose, but I never ran across any in school. I am not asking how to code this, rather approaches to writing the algorithm to do this. I have a few ideas that I'll try, but if there are some standard ways to do this that exist, I'd like to learn about them.
Obviously I'd like to start with Black and White then move into RGBA.
I am looking for any reference material on just this sort of subject. Websites, books, or other references are very very welcome.
Also, if there are different StackOverflow tags that could be good, let me know.
Thanks much!
Edit********** More pics and information
Maybe I wasn't clear what I meant by "unfriendly surfaces". In the previous picture, our blue X's happened to just be a line. Picture a case where it is not a line, rather a wierd shape.
We start with our green pixel traveling at a slope of 2. Suppose it's vector is that of 12 pixels per frame. It would have a projected path like this:
But suppose instead of a nice friendly line, we have this:
In my mind I can kinda of see what is likely to happen if this were a ball and some walls.
Look for edge detection algorithms used in image processing. Some edge detectors also approximate the direction of edges.
You can think of the pixel neighborhood of the green dot, maybe somewhere between 3x3 and 7x7, as a small edge direction detection problem. One approach would take two passes at the pixels:
In the first pass, smooth the sharp black/white pixels using a Gaussian filter.
In the second pass, apply an edge detection operator, such as Sobel, Prewitt or Roberts to produce the X and Y derivatives of the pixels' intensity. You can then approximate the direction as:
angle = arctan(dx/dy)
The motivation for the smoothing pass is to give the edge detection operator information from farther-away pixels.
The Wikipedia page on the Canny edge detector has a good discussion on obtaining the direction (the "gradient") of an edge, including an example of a particular Gaussian filter you can use for smoothing.
Am doing something similar with a ball and randomly generated backgrounds.
The filter and edge detection is highly technical but all other processes using a 5*5 or 3*3 grid seem similarly difficult.
However, I think I may have a cheap way around this. Assuming a ball travelling in any direction, scan all leading edges of the ball - a semicircle. The further to the edge of the ball the collision occurs the closer to vertical is the collision. Again, I think, this should allow you to easily infer the background normal and from there the answer is fairly simple
I am trying to do my own blob detection who will receive a real time video, and try to detect a white paper sheet.
Even if is something written inside the paper. I need to detect the paper and is corner, because what i really want is to draw a opengl polygon over the paper in each corner of the paper will be a corner of the polygon. Then i need the coordinates of the paper to do other stuffs.
So i need to:
- detect a square white blob.
- get the coordinates of the cornes
- draw a polygon over the white sheet.
Any ideias how can i do that?
Much depends on context. For example, suppose that you:
know that the paper is always roughly centered (i.e. W/2, Y/2 is always inside the blob), and no more rotated than 45 degrees (30 would be better)
have a suitable border around the sheet so that the corners never touch the edges of the FOV
are able (through analysis of local variance, or if you're lucky, check of background color or luminance) to say whether a point is inside or outside the blob
the inside/outside function never fails (except possibly in the close vicinity of a border)
then you could walk a line from a point on the border (surely outside) and the center (surely inside), even through bisection, and find a point - an areal - on the edge.
Two edge points give a rect (two areals give a beam), two rects give an intersection (two beams give a larger areal) - and there's your corner. You should carry along the detection uncertainty (areal radius) in order to validate corners (another less elegant approach is to roughly calculate where the corner is, and pinpoint it with a spiral search or drunkard's walk).
This algorithm is amenable to parallelization and, as long as the hypotheses hold, should be really fast.
All that said, it remains a hack -- I agree with unwind, why reinvent the wheel? If you have memory or CPU constraints (embedded systems, etc.), I believe there ought to be OpenCV and e-Vision "lite" ports also for ARM and embedded platforms.
(Sorry for my terminology - I'm monkey-translating from Italian. "Areal" is likely to correspond to your "blob", a beam is the family of lines joining all couples of points in two different blobs, line intensity being the product of distance from a point from its areal's center)
I am trying to do my own blob detection who will receive a real time video, and try to detect a white paper sheet.
Your first shot could be a simple flood-fill. That is, select a good threshold to binarize the image and apply the algorithm. The threshold can be fixed if you know the paper is always brighter than X and the background is always darker than this. Or this can be an adaptive threshold, for example Otsu's method. OpenCV offers this for free.
If you'd need to speed it up you could use a union-find data structure.
Finally you'd need to come up with some heuristic how to identify the corners (e.g. the four extreme values in x/y direction).
Then i need [...] the coordinates of the cornes [...]
Then you don't need blob detection, but corner detection or contour detection in the first place. OpenCV has some nice functionality for exactly this.
If you can't use it, I would suggest to binarize the image as above and use a harris-detector to find the corners of the object.
OpenCV's TBB support could also come quite handy if you'd use it and you have problems to meet your real-time requirements.