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In this paper, a new hybrid system for 3D objects reconstruction is presented. This system is based on the well-known Ball Pivoting Algorithm (BPA) of Bernardini et al. combined with the Radial Basis Function (RBF) model. The BPA can reconstruct surfaces from very large data sets in a little execution time, using small amount of memory, and exhibits linear complexity but it produces holes in case of low sampling density besides it is not capable to fill mis-registration domains. So, the BPA is coupled with the RBF in an integrated system to gain the edge of the RBF as a powerful interpolator for generating surface vertices within severe holes and/or mis-registration domains. Performance and accuracy of the system components are investigated through reconstructing benchmark objects. The results obtained compare favorably with other eminent algorithms. Finally, the proposed system exhibited high robustness in reconstructing objects from real large clouds with non-uniform sampling and mis-registrations.