The three-dimensional (3-D) workflow (acquisition-processing-compression) is, in most cases, sequenced into several independent steps. Such approaches result in an acquisition of an important number of 3-D points. After acquisition, the first processing step is a simplification of the data by suppressing many of the computed points. We propose a coarse-to-fine acquisition system that makes it possible to obtain simplified data directly from the acquisition. By calculating some complementary information from two-dimensional (2-D) images, such as 3-D normals, multiple-homogeneous regions will be segmented and affected for a given primitive class. In contrast to other studies, the whole process is not based on a mesh. The obtained model is simplified directly from the 2-D data acquired by a 3-D scanner.
The 3D chain (acquisition-processing-compression) is, most of the time, sequenced into several steps. Such approaches result into an one-dense acquisition of 3D points. In large scope of applications, the first processing step consists in simplifying the data. In this paper, we propose a coarse to fine acquisition system which permits to obtain simplified data directly from the acquisition. By calculating some complementary information from 2D images, such as 3D normals, multiple homogeneous regions will be segmented and affected to a given primitive class. Contrary to other studies, the whole process is not based on a mesh. The obtained model is simplified directly from the 2D data acquired by a 3D scanner.
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.