## 4 The Proposed Method
### Fast Descriptor Matching
Moreover, we found that the resulting point-cloud model is very dense, which is attributed to the usage of the SPFT feature tracking method. According to our record, the ISFM system with SPFT can recover a high-quality point-cloud model having 338,391 vertices for the HFUT dataset in 5.5 min. As a result, we consider the SPFT has an excellent performance in practice. Figure 4:3 Averaging times for the evaluated methods. Figure 4:4 The point-cloud model for HFUT dataset. Figure 4:5 Samples from HFUT dataset. ### Evaluation of Forensic Dataset

To assess the scalability of the SPFT, we have evaluated it on the Forensic dataset provided by the PIX4D company. The samples of the UAV dataset are provided in Figure 12, which is captured by unmanned aerial vehicle and has large-scale resolution and many repeated features on the surface of each image. In summary, the Forensic dataset is very challenge for feature tracking method and structure from motion. Figure 13 presents the visual correspondences of each feature tracking method for the Forensic dataset, where the SPFT has obtained the maximum number of feature matches, and has the fastest speed among the compared feature tracking methods. The HPM has the minimum number of visual correspondences, and has the lowest speed. According to our statistic, the HPM has an average of 55 feature correspondences on the Forensic dataset. The number of visual correspondences of the MODS in the second place, and it has lower speed than that of the HPM approach because of views synthesis. Although the ENFT has number of visual correspondences less than that of MODS, which has a cheap computational cost. After a deep analysis for the ENFT method, we found that the ENFT heavily depends on the segmentation for input video or image sequences to decrease the computational burden. But, the segmented-based approach easily handicaps the quality of the point-cloud model that is constructed by the SFM system. The SuperPoint has more feature correspondences than that of ENFT, but less than that of ours. However, the proposed SPFT method not only has the cheapest computational cost but also has the highest matching precision among these compared feature tracking methods. According to our statistical results in experiment, the SPFT method has an average of 1876 correct feature matches. In addition to making a comparison with the state-of-the-art method, we have integrated the SPFT into the ISFM system [2], and use the combinational system to estimate the point-cloud model for the Forensic dataset. Figure 14 provides the sparse point-cloud model for the Forensic dataset, which has 2,683,015 vertices and is reconstructed in 10.6 min. We can see that the constructed point-cloud model has good geometric consistency with corresponding to the real scenarios. As a result, we can draw a conclusion that the SPFT has the best performance in both accuracy and efficiency. Figure 4:6 Samples from the Forensic dataset. Note that many repeating features are appeared on the surface of each image. ## 6 Conclusions

In this paper, we proposed an accurate, fast and robust feature tracking method for SFM-based 3D reconstruction, which is based on the superpixel segmentation to increase the number of potentially keypoint and improve the descriptor's quality. In the stage of feature detection, a multiple scale-space analysis and the superpixel-based segmentation technique is used to candidate keypoints, then using

Figure 4:7 The sparse point-cloud model for the forensic dataset, and constructed by the ISFM system with the RTFT feature tracking method. Figure 4:8 Visual correspondences for each method on Forensic dataset. non-maximal suppression technique to remove some unstable keypoints from the initial keypoint collection. In the stage of descriptor computing, we use the segment-based binary test to produce a robust descriptor for each keypoints. In the stage of feature matching, the GPU-accelerated KNN method with ratio-test is used to measure the similarity of two descriptors for saving computation time.