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The 3D acquisition-compression-processing chain is, most of the time, sequenced into independent stages. As resulting, a large amount of 3D points are acquired whatever the geometry of the object and the processing to be done in further steps. It appears, particularly in mechanical part 3D modeling and in CAD, that the acquisition of such an amount of data is not always mandatory. We propose a method aiming at minimizing the number of 3D points to be acquired with respect to the local geometry of the part and therefore to compress the cloud of points during the acquisition stage. The method we propose is based on a new coarse to ne approach in which from a coarse set of 2D points associated to the local normals the 3D object model is segmented into a combination of primitives. The obtained model is enriched where it is needed with new points and a new primitive extraction stage is performed in the rened regions. This is done until a given precision of the reconstructed object is attained. It is noticeable that contrary to other studies we do not work on a meshed model but directly on the data provided by the scanning device.

Coarse to fine: toward an intelligent 3D acquisition system