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In this paper we present a framework for 3D geometric shape segmentation for close-range scenes used in mobile manipulation and grasping, out of sensed point cloud data. Our proposed approach proposes a robust geometric mapping pipeline for large input datasets that extracts relevant objects useful for a personal robotic assistant to perform manipulation tasks. The objects are segmented out from partial views and a reconstructed model is computed by fitting geometric primitive classes such as planes, spheres, cylinders, and cones. The geometric shape coefficients are then used to reconstruct missing data. Residual points are resampled and triangulated, to create smooth decoupled surfaces that can be manipulated. The resulted map is represented as a hybrid concept and is comprised of 3D shape coefficients and triangular meshes used for collision avoidance in manipulation routines.