Inspection analysis of 3D objects has progressed significantly due to the evolution of advanced sensors. Current sensors facilitate surface scanning at high or low resolution levels. In the inspection field, data from multi-resolution sensors have significant advantages over single-scale data. However, most data fusion methods are single-scale and are not suitable in their current form for multi-resolution sensors. Currently the main challenge is to integrate the diverse scanned information into a single geometric hierarchical model. In this work, a new approach for data fusion from multi-resolution sensors is presented. In addition, a correction function for data fusion, based on statistic models, for processing highly dense data (low accuracy) with respect to sparse data (high accuracy) is described. The feasibility of the methods is demonstrated on synthetic data that imitates CMM and laser measurements.