With the widespread application of location-based services, the appropriate representation of indoor spaces and efficient indoor 3D reconstruction have become essential tasks. Due to the complexity and closeness of indoor spaces, it is difficult to develop a versatile solution for large-scale indoor 3D scene reconstruction. In this paper, an annotated hierarchical Structure-from-Motion (SfM) method is proposed for low-cost and efficient indoor 3D reconstruction using unordered images collected with widely available smartphone or consumer-level cameras. Although the reconstruction of indoor models is often compromised by the indoor complexity, we make use of the availability of complex semantic objects to classify the scenes and construct a hierarchical scene tree to recover the indoor space. Starting with the semantic annotation of the images, images that share the same object were detected and classified utilizing visual words and the support vector machine (SVM) algorithm. The SfM method was then applied to hierarchically recover the atomic 3D point cloud model of each object, with the semantic information from the images attached. Finally, an improved random sample consensus (RANSAC) generalized Procrustes analysis (RGPA) method was employed to register and optimize the partial models into a complete indoor scene. The proposed approach incorporates image classification in the hierarchical SfM based indoor reconstruction task, which explores the semantic propagation from images to points. It also reduces the computational complexity of the traditional SfM by avoiding exhausting pair-wise image matching. The applicability and accuracy of the proposed method was verified on two different image datasets collected with smartphone and consumer cameras. The results demonstrate that the proposed method is able to efficiently and robustly produce semantically and geometrically correct indoor 3D point models.
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