To solve the problems that the complexity and closure of indoor scenes lead to the time-consuming and poor coverage of the reconstructed indoor 3D model, a method for indoor structure from motion (SFM) assisted by geomagnetic features is proposed. First, ordinary smartphone sensors were used to obtain indoor images and geomagnetic data. Second, to divide the overall image set into local image sets, a clustering algorithm was used to cluster geomagnetic data, and the clustering results of the geomagnetic data were used as attributes of the corresponding images to obtain the local image sets. Subsequently, the hierarchical SFM was used to construct sparse sub models for each local image set, and the matching points between each sparse sub model were determined. Finally, the RANSAC generalized Procrustes analysis (RGPA) algorithm was used to register local reconstructions and obtain a complete model. Experimental results of indoor reconstruction on the same and different floors show that the proposed method performs well in terms of reconstruction efficiency, reconstruction coverage, and point-cloud generation rate. Compared with the hierarchical SFM method, the proposed method offers a higher reconstruction efficiency by 37% on both datasets, and its reconstruction coverage is closer to the reconstruction target, thus providing a supplementary solution for constructing the same type of indoor environment.