The second step is the reconstruction of the 3D surface illustrated in Figure 3 below. This module uses a set of proprietary algorithms, designed for surface reconstruction and optimization, based on data received from the camera. After receiving raw data (the distorted pattern on the target object), the 3D Reconstruction algorithms perform image filtering (noise reduction), and then instantly reconstructs the 3D surface, smoothing and interpolating data to avoid holes and optimizing the mesh.

The algorithm has to recognize the pattern projected onto the surface and calculate, by means of triangulations, all three coordinates of the sampled points on the surface. This will result in the surface described in the form of a cloud of points. After this step, the system will interpolate all the points by mean of a mesh.

Next, if the color surface was captured by a Bioscrypt enrollment device, the surface can then be calculated and over-imposed onto the mesh. The texture can be overlapped (after an automatic adaptation) on the 3D surface. This stage is not relevant for devices using the 3D video unit, where the surface texture is not captured.

Figure 3 - Flow scheme of the 3D reconstruction process.