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This paper deals with a global intelligent 3D digitizing algorithm, which allows increasing the quality of the resulting cloud of points. Built from quality analysis and characteristic line extraction, the algorithm computes a new path belonging to an admissible space with the objective of increasing the quality of the new resulting points cloud. The extraction work is performed thanks to a scale space algorithm, based on an iterative projection algorithm and the concept of mean curvature motion (MCM). The scale space framework allows us to perform the detection at a coarse scale without any noise or digitizing error interference and to project the result back onto the original point cloud. Thus all the details of the real object’s shape can be identified. Several applications illustrate geometrical feature extraction and global intelligent 3D digitizing within the context of RE. An application is also proposed to compute the distance between the real object shape and an existent CAD-model for con...

Feature Extraction from High-density Point Clouds: Toward Automation of an Intelligent 3D Contactless Digitizing Strategy