The fast estimation of volumes of landslide deposit has great significance for emergency rescue decision-making, such as deposit filling and excavation and road dredging. The existing methods for the volumes of landslide deposit estimation have the disadvantages of a long data acquisition cycle, poor timeliness and high cost. Therefore, this paper researches a fast estimation method of volumes of landslide deposit based on the 3D reconstruction of smartphone images. First, the improved algorithms of speeded up robust features (SURF) and oriented fast and rotated brief (ORB) algorithms are combined to realize the fast matching of landslide feature points. Second, the 3D scene sparse reconstruction of landslide deposit is realized through boundary constraints. Third, taking the steepest gradient into account, the interpolation of the bottom of the landslide deposit and the fast estimation of the volumes are carried out. Finally, a prototype system is developed, and a case study is performed. The experimental results show that the estimation results of the proposed method are between those of the commercial software ContextCapture, Metashape and Pix4Dmapper, which are commonly used in the field of 3D reconstruction, but the estimation speed is 1–2 times faster than that of the commercial software mentioned above. The proposed method can realize the low-cost and fast estimation of landslide deposit volumes and provide technical support for landslide emergency rescue.