02 Nov From point cloud to STL triangulated surface
Sets of coordinates originating from a 3D scanner or discrete samples of a surface, cloud points can come from very different sources. One of the classical operations one wants to perform on such an input is to recreate the original surface these points represent. It is a complex elaboration that has limitations and shortcomings. The tool we present here tries to provide the best output under certain conditions on the input samples and the original geometry. Find detailed info below.
The reconstruction problem
The problem of reconstructing a surface from a point cloud is ill-defined. Because the points only provide a sample of the surface, it is not possible to accurately determine how the surface behaves between the points. Sampling the surface further provides more information, but still the problem remains on a smaller scale.
In case of real-world data sets, noise and outliers caused by small errors in the point location and points that do not sample the surface respectively, must also be considered. These additional issues are generally caused by an inaccurate measurement procedure and when the sampled surface is unknown (that is in general the case), it is impossible to determine whether a distinctive point is an outlier, is caused by noise, or samples a small feature of the surface.
Parameters set and Output surface
The surface reconstruction performed by this tool needs three parameters: the mean number of neighbors, the number of samples and the number iterations of increasing the scale. Default values usually work well on a broad spectrum of data sets, however, we advise to carefully fine-tune these parameters for each type of data set.
In general, an average of 30 neighbors gives good results. This number should be increased in case of very low point density zones, and decreased if one wants to better reconstruct thin features of the object. The number of samples is related to how regularly the points cover the object. A large sample is required if the object is covered very irregularly, but this implies more computation time. 200 samples is a good default choice.
The number iterations of increasing the scale is related to the cloud point noise, surface features acuteness and object thickness. More iterations are required for points with a lot of noise and objects with sharp or small features. But processing too many iterations can degenerate a volume into a plane. This trend may cause the reconstructed surface to connect points on opposite sides of the object. Generally, 4 iterations are appropriate.
“Upload your point cloud and let us build on it the correct triangulated surface.”
Input, output and how to use it
Using “Points to STL” tool is very straightforward. The input file can be both an Object File Format file (*.off) or a Comma Separated Value file (*.csv) where 3D point coordinates are stored.
The STL output file contains a surface mesh that is non-self-intersecting. However, it won’t necessarily be 2-manifold: an edge can be shared by more than two triangles and triangles may overlap exactly (with opposite orientation) if large regions on both sides of the triangle are empty of points. Generally, when the points sample the surface of an object, the computed surface will contain a thin volume between an outward-facing and an inward-facing surface. The triangulated surface will not have edges shared by only one triangle or holes and the triangles are all oriented away from the point set.
Points to STL | How to use it
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