I would like to generate visually appealing surface reconstruction from the the point clouds.
I am using point cloud library. I tried creating a mesh using poisson reconstruction method but later found that it gives a water tight reconstruction.
For example: In my case I have a point cloud of a room
Using the code at http://justpaste.it/code1 , I was able to get a reconstruction like this
(source: pcl-users.org)
The above picture has the surface which is covering the top view. This was visualized using MeshLab.
Then later on the MeshLab GUI when I press points, it looks like this.
(source: pcl-users.org)
But in the second picture there are points on its surface too(Not clearly visible in the attached picture).
Can you help in creating a model that has no points on the top and just has the inside structure ?
Any other suggestions to improve the reconstruction quality ?
The point cloud of the room and generated ply file can be downloaded from https://dl.dropboxusercontent.com/u/95042389/temp_pcd_ply_files.tar.bz2
One solution that works for me is obtaining a convex/concave hull of your point cloud. Then you can use this hull to filter/crop your mesh after Poisson reconstruction. If you use the PCL you can try ConvexHull or ConcaveHull together with CropHull and test the results. Hope this solves your issue, it did for me.
As far as my experience is concerned (meshing caves), meshing with Poisson will result in watertight model/mesh, which is why your model was covered entirely. I only deal with meshes using MeshLab but I am guessing it is the same thing. What I did try is using Ball-Pivoting meshing algorithm in MeshLab which result in non-watertight model. Maybe that is what you are looking for.
Related
I'm working on a project to detect the position and orientation of a paper plane.
To collect the data, I'm using an Intel Realsense D435, which gives me accurate, clean depth data to work with.
Now I arrived at the problem of detecting the 2D paper plane silhouette from the 3D point cloud data.
Here is an example of the data (I put the plane on a stick for testing, this will not be in the final implementation):
https://i.stack.imgur.com/EHaEr.gif
Basically, I have:
A 3D point cloud with points on the plane
A 2D shape of the plane
I would like to calculate what rotations/translations are needed to align the 2D shape to the 3D point cloud as accurate as possible.
I've searched online, but couldn't find a good way to do it. One way would be to use Iterative Closest Point (ICP) to first take a calibration pointcloud of the plane in a known orientation, and align it with the current orientation. But from what I've heard, ICP doesn't perform well if the pointclouds aren't kind of already closely aligned at the start.
Any help is appreciated! Coding language doesn't matter.
Does your 3d point cloud have outliers? How many in what way?
How did you use ICP exactly?
One way would be using ICP, with a hand-crafted initial guess using
pcl::transformPointCloud (*cloud_in, *cloud_icp, transformation_matrix);
(to mitigate the problem that ICP needs to be close to work.)
What you actually want is the plane-model that describes the position and orientation of your point-cloud right?
A good estimator of your underlying function can be found with: pcl::ransac
pcl::ransace model consensus
You can then get the computedModel coefficents.
Now finding the correct transformation is just: How to calculate transformation matrix from one plane to another?
Suppose I have a 3D model that roughly resembles a cube or a cuboid, and I wanted to estimate a regular set of gridlines that lays on top of the 3D model.
Is there an efficient way of doing this?
A brute force way might be to first estimate the 8 corner points, then use the geodesic function in CGAL to link up the corner points to form the initial path lines. Recursively, take the halfway point in each path find the geodesic path to the halfway point of the opposing path.
But that would take up too much computing time.
Is there a faster way to do this via texture mapping texture? I need to code this out so will be thankful if a specific algorithm can be referred to, rather than using a software such as blender, maya, etc.
thanks for any help!
I'm trying to use Polygonal Surface Reconstruction with building point cloud to create simplified building models.
I did first tests with this CGAL code example and got first promising results.
As an example, I used this point cloud with vertex normals correctly oriented and got the following result from PSR. Some faces are clearly inverted (dark faces with normals pointing inside the watertight mesh and therefore not visible).
I was wondering if there a way to fix this face orientation error. I've noticed orientation methods on Polygon mesh but I don't really know to apply them to the resulting PSR surface mesh. As far as logic is concerned making normal point outwards should not be too complicated I guess.
Thanks in advance for any help
You can use the function reverse_face_orientations in the Polygon mesh processing package.
Note that this package has several functions that can help you to correct/modify your mesh.
I hope to find some hints where to start with a problem I am dealing with.
I am using a Kinect sensor to capture 3d point clouds. I created a 3d object detector which is already working.
Here my task:
Lets say I have a point cloud 1. I detected a object in cloud A and I know the centroid position of my object (x1,y1,z1). Now I move my sensor around a path and create new clouds (e.g. cloud 2). In that cloud 2 I see the same object but e.g. from the side, where the object detection is not working fine.
I would like to transform the detected object form cloud 1 to cloud 2, to get the centroid also in cloud 2. For me it sound like I need a matrix (Translation, Rotation) to transform point from 1 to 2.
And ideas how I could solve my problem?
Maybe ICP? Are there better solutions?
THX!
In general, this task is called registration. It relies on having a good estimation of which points in cloud 1 correspond to which clouds in point 2 (more specifically, which given a point in cloud 1, which point in cloud 2 represents the same location on the detected object). There's a good overview in the PCL library documentation
If you have such a correspondence, you're in luck and you can directly compute a rotation and translation as demonstrated here.
If not, you'll need to estimate that correspondence. ICP does that for approximately aligned point clouds, but if your point clouds are not already fairly well aligned, you may want to start by estimating "key points" (such as book corners, distinct colors, etc) in your point clouds, computing a rotation and translation as above, and then performing ICP. As D.J.Duff mentioned, ICP works better in practice on point clouds that are already approximately aligned because it estimates correspondences using one of two metrics, minimal point-to-point distance or minimal point to plane distance, according to wikipedia, the latter works better in practice, but it does involve estimating normals, which can be tricky. If the correspondences are far off, the transforms likely will be as well.
I think what you were asking about was in particular to the Kinect Sensor and the API Microsoft released for it.
If you are not planning to do reconstruction, you can look into the AlignPointClouds function in Sensor Fusion namespace. This should take care of it automatically, in methods similar to the answer given by #pnhgiol.
On the other hand, if you are looking at doing reconstruction as well as point cloud transforms, the Reconstruction class is what you are looking for. All of which can be found out about, here.
I have a set of points in 3D that lie on a surface and I also have the normals at every point.
I would like to generate a surface triangulation with this information. In addition I could tell the algorithm to use what points lie on the boundary if that is needed.
So, I have quite a bit of information:
* points
* normals
* boundary
How do I triangulate a surface with this information using vtk?
A surface reconstruction algorithm is like using a bomb for this problem since I have all this information that I would like to use. This information comes from a simulation so I know the surface exists and that is quite smooth.
I would like the answer to be cast in terms of either what vtk function to use and if available (and that would be great) examples using this function.
Thank you so much in advance.
You can use the vtkSurfaceReconstruction filter to create a surface from a set of 3D points.
You could try the point cloud library
Point Cloud Library
Just the 3D points would be good enough. Since you know that your surface is smooth, you can perform a Delaunay triangulation of the points (vtkDelaunay3D) and apply a subdivision filter for smoothening (vtkButterflySubdivisionFilter).
Delaunay3D triangulation