Abstract
This paper describes a new and fast method for reconstructing a 3D computerized model from a cloud of points sampled from the object's surface. The proposed method aggregates very large scale 3D scanning data into a Hierarchical Space Decomposition Model (HSDM), realized by the Octree data structure. This model can represent both the boundary surface and the interior volume of an object. Based on the proposed volumetric model, the boundary reconstruction process becomes more robust and stable with respect to sampling noise. The hierarchical structure of the proposed volumetric model enables data reduction, while preserving critical geometrical features and object topology. As a result of data reduction, the execution time of the reconstruction process is significantly reduced. Moreover, the proposed model naturally allows multiresolution boundary extraction, represented by a mesh with regular properties. The proposed surface reconstruction approach is based on Connectivity Graph extraction from HSDM, and facet reconstruction. This method's feasibility will be presented on a number of complex objects.
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