Abstract: With the rapid development of the digital economy,the demand for 3D reconstruction technology has significantly increased.However,existing commercial 3D reconstruction systems often rely on closed standalone or cluster architectures,which limit flexibility and efficiency,while open-source frameworks face deficiencies in absolute coordinate and scale recovery.This paper introduces an SFM algorithm based on a GCP assisted Colmap framework to address these issues.The algorithm precisely converts the free network results of SFM in Colmap to absolute coordinates through constructing residual equations,applying similarity transformation,and global bundle adjustment.Experimental results show that this method achieves computational accuracy comparable to commercial systems like Agisoft and DJI Terra,while maintaining high computational efficiency in scale reco-very.This study not only enhances the absolute scale recovery capabilities of open-source 3D reconstruction systems but also lays the theoretical and practical foundations for future cloud-based applications and large-scale data processing.Future efforts will focus on realizing a fully automated cloud architecture for 3D reconstruction and exploring its application prospects in 3D monitoring with IoT devices.

Key words: 3D reconstruction, Digital economy, GCP-assisted SFM, Absolute scale recovery, COLMAP open source framework, Cloud computing applications