I want to reconstruct a surface from a point cloud of xyz points only, but the example provided requires a normal for each point. I have 2 questions: 1) what does the normal represent. 2) what do I do if I don't have the normal.
If you have a point cloud that represent a surface, the associated normal is the normal direction on the surface at the point. If you don't have such normals, CGAL provides some methods to estimate and orient those normals:
pca_estimate_normals() or jet_estimate_normals() for the estimation and mst_orient_normals() for the orientation.
Note that the links I'm giving are for the upcoming 5.1 release but the functions exist in previous releases. You can also read the new reconstruction tutorial here.
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I draw a vectorial geometry with some calibration points around it.
I print this geometry and then I physically scan the printed calibration points (I can't scan the geometry, I can only scan the calibration points).
When I acquire these points, these aren't in their position anymore because of some print error or bad print calibration.
The question is:
Is there any algorithm that helps me to adapt the original geometry in base of the new points scanned?
In practice I need to warp the geometry in order to obtain the real geometry printed on the paper with the same print error that I have on the calibration points.
The distortion is given by the physical distortion of the material (not paper but cloth) during the print process. I can't know how much the material will distort during the print.
Yes, there are algorithms to help you with that. In general you need to learn/find the transformation between the two images that you have.
Typical geometrical transformations are affine transformations (shift, scale, rotation, shear, reflections) which need at least three control points or piecewise local linear/ local weighted mean which need at least 4-6 control points. The more control points you have, the better in general.
Given a set of control points in one image and the corresponding set of control points in the other image there are algorithms for finding the optimal transformation between if you specify a class (affine or piecewise local linear). See for example fitgeotrans in Matlab. I don't know how exactly it solves the problem by I guess by some kind of optimization. It should be easy to find available implementations for other programming languages (Python, C, Java).
What remains is finding the correspondence between the control points in the two images. For a few images you may be able to do that by hand, but in the general case you might want to automatize this as well. General image registration algorithms like imregister should do well for your images. They give you a good initial estimate for the transformation (may already be sufficient) so that then identification of the corresponding point pairs is trivial (always take the nearest) and allow refining.
So I advice you to first just try to register the images (gray scale data) with an identity transformation as starting value. Then identify corresponding point pairs and refine the transformation either using an affine or a piecewiece/local transformation. Then apply the transformation on the geometry to get the printed geometry. Depending on your choice of programming languages you will find many implementations that do the job.
I'm running a model-scene match between a set of point clouds in order to test the matching results.
The match is based on 3D features such as normals and point feature histogram.
I'm using the normal estimation of point cloud library (pcl) to compute the histogram after I'd resampled the point cloud of both model and scene.
My question is, how can I test the accuracy of selecting different radius values in the nearest-neighbor estimation step.
I need to use that values for normal estimation, resampling and histogram in objects such as cup/knife/hummer etc.
I tried to visualize those objects using the pcl visulizer with different radius values and choosing which one that gives correct normals (In terms of how perpendicular were the normals orientation to the surfaces).
But I think that this visual testing is not enough and I would like to know if there are some empiric ways to estimate the optimal radius value.
I would appreciate any suggestion or help ,share your thoughts :)
Thank you.
I think you should start from a ground test: create a point cloud from a mesh using the mesh normals (using CloudCompare for example), then load it twice: once with full data (including normals) and once without normals.
Rebuild normals using the search radius to be tested then you can directly compare de obtained normals with the one extracted from the mesh...
I'm maintaining software which uses PCL. I'm myself not much experienced in PCL, I've only tried some examples and tried to understand the official PCL-Ducumentation (which is unfortunately mainly sparse, doxygen-generated text). My impression is, only a PCL contributors have real change to use the library efficiently.
One feature I have to fix in the software is aligning two clouds. The clouds are two objects, which should be stacked together with a layer in-between (The actual task is to calculate the volume of the layer ).
I hope the picture explains the task well. The objects are scanned both from the sides to be stacked (one from above and the other from below). On both clouds the user selects manually two points. Then, as I hope there should be a mean in PCL to align two clouds providing the two clouds and the coordinates of the points. The alignment is required only in X-Y Plane.
Unfortunately I can't find out which function should I use for this, partly because the PCL documentation is IHMO really humble, partly because of lack of experience.
My desperate idea was to stack the clouds using P1 as the origin of both and then rotate the second cloud manually using the calculated angle between P11,P21 and P12,P22. This works, but since the task appears to me very common, I'd expect PCL to provide a dedicated function for that.
Could you point me to a proper API-function, code-snippet, example, similar project or a good book helping to understand PCL API and usage?
Many thanks!
I think this problem does not need PCL. It is simple enough to form the correct linear equation and solve it.
If you want to use PCL without worrying about the maths too much (though, if the above is a mystery to you, then studying some computational geometry would be very useful), here is my suggestion.
Most PCL operations work on 3D point clouds. I understand from your question that you only have 2D point clouds OR you don't care about the 3rd dimension. In this case if I were you I would represent the points as a 3D point cloud and set the z dimension to zero.
You will only need two point clouds with 3 points as that is how many points you are feeding to the transformation estimation algorithm. The first 2 points in the clouds will be the points chosen by the user. The third one will be any point that you have chosen that you know is the same in both clouds. You need this third one otherwise the transform is still ambiguous if it is a general transform that is being computed. You can calculate however such a point as you know 2 points already and you know that all the points are on a common plane (as you have projected them by losing the z values). Just don't choose it co-linear with the other two points. For example, halfway between the two points and 2cm in the perpendicular direction (ensuring to go in the correct direction).
Then you can use the estimateRigidTransformation functions to find the transform.
http://docs.pointclouds.org/1.7.0/classpcl_1_1registration_1_1_transformation_estimation_s_v_d.html
This function is also good for over-determined problems (it is the workhorse of the ICP algorithm in PCL) but as long as you have enough points to determine the transform it should work.
I am working with disparity maps (1024 x 768) obtained via stereo and I am able to get point clouds with XYZRGB pcl::Points. However not all pixels from the disparity map are valid depth hence there will never be 1024x768 = 786432 XYZRGB points. Fortunately I am able to save the point clouds unorganized (i.e. height=1). Unfortunately, some normal estimation methods etc, are tailored for organized pointclouds. How can I create organised pointclouds from this ?
I believe that this is not possible.
First of all unorganized point cloud (PC) is just list of points in random order written in file
On the other hand organized PC carries information of in which order orginal points were obtained by depth camera and some other information. This information is stored in lets call it grid.
Once you destroy this grid omiting some points theres no algorithm that can put it back together as it originally was
You can use other methods which provides PCL that doesnt take OPC as an argument. Result will be same as if you would use organized point cloud only little bit slower (depends on size of your input cloud)
I assume that you do have the calibration parameters that are necessary to transform the image points and their depth into 3D points, right?
In this case, you simply create a 2D point cloud and do the following for each pixel of the disparity map:
If the point is valid:
set the corresponding point in the point cloud to the 3D point
else:
set the corresponding point in the cloud to NaN (i.e. a 3D point with NaN as coordinates)
I have a city square with people, cars, trees and buildings in pcl format. I need to automatically determine the ground plane and project this objects on that ground plane to get a 2D map with occupied places.
Any idea?
I think the best thing to do here would be to familiarise yourself with the following two PCL tutorials:
http://pointclouds.org/documentation/tutorials/planar_segmentation.php
http://pointclouds.org/documentation/tutorials/project_inliers.php
The first tutorial makes use of the RANSAC algorithm to find a dominant plane in a scene. I use it to find tables and floors in robotics scenarios. You would use it to find your dominant ground plane.
The second tutorial shows how to project points directly onto a plane. This is what you would use to make your 3D point cloud into a 2D one. Note that, despite the "inlier" keyword, you can pass your whole point cloud to be projected onto the plane.
Actually, if you are after "occupied" places, you might want to project all of the points that aren't in the ground plane (i.e. the outliers), and that are above it (you can use a PCL filter, such as PlaneClipper3D, for example, or just the complement of the outliers from the plane-segmentation operation.
If the plane that you end up with (containing all your projected points) is not in the coordinate frame you want, you may wish to rotate the whole lot, for example, to align with the coordinate axes so that all z-coordinates are zero. See pcl::transformPointCloud for this (the transform will be obtainable from the plane coefficients returned from the plane segmentation).
I hope this is helpful and not at too basic a level, though the question was rather general so I suppose it should be okay.