The accuracy of a 3D scanner is given by the closeness of its 3D point measurements of a scanned object to the actual values. Here, one of the most straightforward 3D structures for evaluation is a sphere lying on a planar surface.
By scanning the sphere and comparing the measured 3D point cloud with their parametric 3D model, the error for each point in 3D space is precisely estimated. For a standard LilScan 3D scanner, a standard deviation of 0.07mm or 70 microns is achieved. This result means, by assuming a Gaussian distribution, 95% of all measurements are inside an error interval of two-sigma or +-0.14mm.
Or in other words, with LilScan you can easily scan a golf ball and count every dimple in the resulting 3D scan.
