Abstract: 3D scanning of scene-level targets usually confronts several bottlenecks including a large amount of redundant data, feature drifting as well as time-consuming. To solve these problems, a scene-level targets reconstruction method is proposed based on the sparse sequence fusion. The first step is to construct the supporting subsets via sampling the depth image sequence. Second, the supporting subsets are divided into a set of successive fragments. To optimize the camera motion trajectory, geometric feature is introduced to the process of the continuous iterative registration between multiple fragments. Finally, fusing the supporting subsets could generate the targeted surface. The scanning tests and the comparison experiments are conducted on depth sequences captured by a consumer depth camera and two public datasets, SceneNN and Stanford 3D Scene. The results show that the proposed method can reduce the registration RMSE by 16%~28%, use only 8%~54% data, and shorten the running time by about 56%. In addition, it enhances the effectiveness as well as robustness of 3D scanning and improves the reconstructed quality significantly.