“Filters -> Point Set -> Compute Normals for point set” We will now have to calculate the normals on the sub-sample we just created so MeshLab knows which side of the point is facing “out” and which is “in”. It is also important to note that since the Poisson is a stochastic process no two subsamples will be exactly the same even if the exact same parameters are used. When points are determined to be statistically random following the number of iterations you specify the alogritim will remove that point from the recreation of the surface.Įven though the Poisson does an excellent job there are still cases where manually cleaning these points from the data is required. Much like the filtering of noise in LiDAR data the Poisson takes the entire area of interest(the radius of the window size we specify in this case) and looks at the corresponding distribution of points in 3D space. While there are many different ways to deal with these rouge points we can once again apply the Poisson distribution, which seems to have the best results in the automated filters offered by MeshLab. So to avoid have spikes or deformities in our data we should apply a few methods in eliminating them when possible.įalse points to be removed from point set data More on Subsampling The image below the point cloud captured from the Microsoft Kinect (of a human chest – side view) and it has points that are not apart of the actual object we want to creat a 3D model of. Meaning that what worked well with a point cloud of a million points for the interior of a room, may not work with a million points of a human face. Like previously mentioned the exact parameters used in your process are TOTALLY APPLICATION DEPENDENT. The algotrithim it was designed to create circular window over the point cloud and calculate those points that are statistically “random” according to a Poisson distribution. Make sure you check the “Base Mesh Subsampling” box. The “Filter->Sampling->Poisson Disk Sampling”
#MESHLAB MEASURING TOOL TRIAL#
We will want to recreate a surface, which through trial and error (at least with objects that contain a lot of curves or contours) the Poisson disk method obtains the best results. *** Especially in noisy scan’s from the Kinect This does inevitably reduce the resolution of the data but if proper techniques are used you can maintain a high level of fidelity in the point cloud data. Occasionally you will need to sub-sample your point-cloud data to make it easier to work with. PLY, STL, OFF, OBJ, 3DS, COLLADA(dae), PTX, V3D, PTS, APTS, XYZ, GTS, TRI, ASC, X3D, X3DV, VRML, ALN **MeshLab can import the following file types: Once MeshLab is open the “Import Mesh” icon on the main toolbar will allow you to navigate to the files you have stored.
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