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Abstract Extendable booms for deploying payloads, solar sails, and antennas are promising, but developing a correlated modal analysis model of these sensitive systems using conventional methods remains challenging. To address this issue, we have taken a novel reverse engineering approach for integrating high-density and -accuracy 3D-scanning geometric data into a finite element analysis (FEA) model, reducing errors relative to conventional methods. Ut ProSat-1 (UPS-1), a 3U CubeSat built by Virginia Tech, aims to repeatedly passively self-deploy a parabolic tape spring boom on orbit to characterize boom dynamics experimentally. However, conventional modeling methods were not sufficient to capture the boom’s modal parameters, which are highly sensitive to geometry defects. Here, we develop a methodology to reduce geometric errors that affect the modal response of the boom by using a novel method to generate a high-density, high-accuracy 3D point cloud from a laser scanner and build a corresponding FEA model and verify that method relative to experiments. Three FEA models were created: an ideal geometry case, a geometry developed from discrete measurement of the boom’s cross section along its length, and a high-fidelity geometry developed from a 3D laser scanner-derived point cloud. The method used in this effort is an efficient and accurate method to generate an FEA Point Cloud Model based on 3D laser scanner data. The Point Cloud Model was shown to achieve a position error of 0.1 mm, while the idealized geometry and measured models had a much larger error of 13 mm and 7 mm, respectively. The Point Cloud Model resulted in a first mode frequency error of only 1.2% relative to the 7.7% error for the discrete modeling method and 27% for the ideal constant cross section geometry.