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This paper presents an integrated framework for surface reconstruction capable of handling large scale clouds of points. This framework is based on two proposed methods for implicit surface fitting and polygonization to convert a cloud of unorganized points into an optimized surface. The proposed fitting method employs the partition of unity (POU) method associated with the radial basis functions (RBF) over a distributed computing environment to facilitate and speedup fitting of large scale clouds without any data reduction to preserve all the surface details. Moreover, an innovative adaptive mesh refinement (AMR) based method is proposed for implicit surface polygonization. This method steers adaptive volume sampling via a series of optimization criteria to provide accurate and optimized surfaces with minimum number of polygons. The experimental results for the considered test models showed an average reduction of 60% in fitting time using 16 processing nodes and 90% in polygonization time on the master node only against other traditional methods with better performance.