I am using PCL to estimate the normals of point cloud data. It returns to me a vector with four elements the first three are varying between -1 and 1. How can I cluster the point cloud data according to the normals.
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I have an equipment which performs radial scan. It scans an object along the green lines showing in the image(three lines in the image, but the equipment can perform more).
Thus, I can get a point cloud, which contains points from the upper and lower surfaces of the object. And I want to make surface reconstruction using the point cloud.
I load the point cloud file(.txt format, xyz coordinates of each point) into Meshlab and it shows like this(yellow points are the points in the point cloud):
I then followed a blog teaching simple usage of Meshlab and clicked "Fiter——>Normals,Curtavures and Oreientation——>Smooths normals on a point set" and "Fiter——>Remeshing Simplication and Reconstruction——>Surface Reconstruction：Ball Pivoting". (both default setting)
However, the results was not I want:
It connects points within a scanned image, but the surface should be reconstructed by connecting points between adjacent scanned images.
I can think about two possible reasons:(1) I did not choose right setting in Meshlab. If so, which setting parameters can make reconstruction for the point cloud. (2)My point cloud is too sparse and I need to interpolate the point cloud to make it have more points and which interpolation method should I use?
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The normals computed in this image are computed using Neighbour num 10 and smooth iteration 8.
And, the normals computed in this image are computed using Neighbour num 60 and smooth iteration 8. When Neighbour num is greater than 20, normals are similar to the image below.
It is possible that you are having problems due to inequality directional sampling of the surface. You have high density of points in one direction, then a big gap in the other direction. This is a problem due to 'Compute normals for point sets' filter uses a parameter with the number of Neighbours to each point, so the computer normal will be byassed because there is unlikely to find neighbours in a different scanline.
So my proposal is (I will reuse parts from this tutorial )
Point Cloud Simplification and Normals Computation
Start by increasing the number of orientations in the scan. You want to fill those gaps.
If you need to reduce the number of point samples in the center of the object to reduce noise, go to Filters -> Point Set -> Point Cloud Simplification. Make sure Best Sample Heuristic is checked.
After point cloud simplification, make sure to select Simplified point cloud in the Show Layer Dialog on the right hand side. If not visible, it can be opened by navigating to View -> Show Layer Dialog. Now we need to compute normals for point set.
So go to Filters -> Point Set -> Compute normals for point sets . Enter Neighbour num between 10 - 100. Initially try with 10 and try to get a mesh and later see if this can be improved by increasing the neighbour number. For Smooth Iteration initially try with 0 value and may be later it can be tried with values between 5 - 10. I mostly use value 8.
Make sure if your normals are properly computed by going to Render -> Show Normal.
Meshing / Poisson Surface Reconstruction
Next we are going to use Poisson Surface reconstruction to do meshing.
So go to Filters ->Remeshing, Simplification and Reconstruction -> Screened Poisson Surface Reconstruction. Initially try with default parameters then later one can play around with reconstruction depth, number of samples and interpolation weight values.
This will create another mesh layer called Poisson in the Show layer Dialog which has surfaces now. Make sure to select that to peform further operations.
One can observe that it has also created some extra surfaces. To remove them go to Filters -> Selection -> Select Faces with edges longer than .... By default the value is automatically computed, just click on apply. Then click on delete face button (triangle face and three vertex with a cross over it). This will remove extra surfaces.
After this operation, still some noise faces can be seen. To remove them go to Filters -> Cleaning and Repairing -> Remove isolated pieces (wrt Face Num.). Use the default value and make sure Remove unreferenced vertices is checked. This will remove some noise faces.
Even after the above operation some noise faces are seen. To remove them go to Filters -> Selection -> Select non Manifold Vertices. Click apply. Then click on delete face button (triangle and threwe vertex with a cross over it). This will remove remaining extra faces.
I am working on a application that is filtering a point cloud from a laser distance measuring device. Its a small array only 3x176x132 and Im trying to find parts inside a bin and pick the top most. So far I have played around with filtering the data into a way that can be processed by more traditional vision algorithms. I have been using the Sobel operator on the distance data and normalizing it and this is what I came up with
The Same filter applied to the PMD amplitude
My problem is I feel like I am not getting enough out of the distance data. When I probe the actual height values I see a drop the thickness of a part around the edges but this is not reflected in the results. I think it has to do with the fact that the largest distance changes in the image are 800mm and a part is only 10mm but Im sure there must be a better way to filter this.
Any suggestions
I am modifying the tango example point cloud app.
I have exported point clouds along with its current pose data.
The point cloud coordinates we get are relative to current pose.
I wanted to know how can I convert the point cloud of different poses to worldspace coordinates (with respect to origin which should be first pose in this case)?
1 - Applying the pose transform to the pose points will give you world space coordinates
2 - If you're treating the very first pose as special (which I really wouldn't advise) then you are expressing your coordinates in the first poses coordinate system, not world coordinates. If you really want to do this (please don't, simply computing inverse transforms is far better) then I'd say you want to invert the first transform and keep it handy, and then multiply subsequent transforms by the inverse of the first transform (to cancel out the 'contribution' of the first) and then transform the points with the result.
How do I get the kinect facetracking mesh?
this is the mesh: http://imgur.com/TV6dHBC
I have tried several ways, but could not make it work.
e.g.: http://msdn.microsoft.com/en-us/library/jj130970.aspx
3D Face Model Provided by IFTModel Interface
The Face Tracking SDK also tries to fit a 3D mask to the user’s face.
The 3D model is based on the Candide3 model
(http://www.icg.isy.liu.se/candide/) :
Note:
This model is not returned directly at each call to the Face Tracking
SDK, but can be computed from the AUs and SUs.
There is no direct functionality to do that. You have to use the triangle and vertex data to generate the necessary vertex and indices lists that are required.
GetTriangles method gets you the faces (indexes of the vertices of the triangles in a clockwise fashion), and then from using these indexes for the array of vertices to get the 3d model. Array of vertices has to be reconstructed every frame from the AUs and SUs with Get3DShape or GetProjectedShape (2D) functions.
For more, search for IFTModel (http://msdn.microsoft.com/en-us/library/jj130970.aspx) and for visualizeFaceModel (a sample code, which can help in understanding the input parameters of get3DShape).
(This sample uses the getProjectedShape, but the input parameters are nearly identical for both functions)
I'm sure the opposite has been asked many times but I couldn't find any answers on how to generate bad random numbers.
I want to write a small program for cluster analysis and want to generate some random Points for testing. If I would just insert 1000 Points with random coordinates they would be scattered all over the field which would make a cluster analysis worthless.
Is there a simple way to generate Random Numbers which build clusters?
I already thought about either not using random() but random()*random() which generates normally distributed numbers (I think I read this somewhere here on Stack Overflow).
Second approach would be picking a few areas at random and run the point generation again in this area which would of course produce a cluster in this area.
Do you have a better idea?
If you are deliberately producing well formed clusters (rather than completely random clusters), you could combine the two to find a cluster center, and then put lots of points around it in a normal distribution.
As well working in cartesian coords (x,y); you could use a radial method to distribute points for a particular cluster. Choose a random angle (0-2PI radians), then choose a radius.
Note that as circumference is proportional radius, the area distribution will be denser close to the centre - but the distribution per specific radius will be the same. Modify the radial distribution to produce a more tightly packed cluster.
OR you could use real world derived data for semi-random point distributions with natural clustering. Recently I've been doing quite a bit of geospatial cluster analysis. For this I have used real world data - zipcode centroids (which form natural clusters around cities); and restaurant locations. Another suggestion: you could use a stellar catalogue or galactic catalogue.
Generate few anchors. True random numbers. Then generate noise around them:
anchor + dist * (random() - 0.5))
this will generate clustered numbers, that will be evenly distributed in distance dist.
Add an additional dimension to your model.
Draw an irregular (i.e. not flat) surface.
Generate numbers in the extended space.
Discard all numbers which are on one side of the surface.
From every number left, drop the additional dimension.
Maybe I have misunderstood, but the gnu scientific library (written in c) has many distributions written within it - could you not pick coordinates from the Gaussian/poisson etc from that library?
http://www.gnu.org/software/gsl/manual/html_node/Random-Number-Distributions.html
They provide a simple example with the Poisson distribution from the link, too.
If you need your distribution to be bounded (for example y-coordinate not less than -1) then you can achieve that by rejection sampling from the uniform distribution in the gsl.
Blessings, Tom
My first thought was that you could implement your own using a linear congruential generator and experiment with the coefficients until you get a low enough period to suit your needs. A really low m coefficient should do the trick.
I also like your second idea of running a good RNG around a few pre-selected points to create clusters. You could either target specific areas for the clusters with this method, or generate those randomly as well.