## 4 The Proposed Method
### Fast Descriptor Matching
Moreover, we have tallied up the matching precision for each feature tracking method, the statistical results are depicted in Figure 8. Among those methods [77, 78, 79, 80], the proposed SPFT has the highest matching precision, namely the matching precision is 85.6%; The SuperPoint is in the second place; The ENFT is in the third place; and the matching precision of HPM is the lowest. The matching performance of MODS is better that that of HMP and ROML. According to this experiment, we have the following valuable findings: (1) ENFT have robustness to rotation change due to the usage of SIFT feature; (2) The viewpoint change has a significantly impact on the matching precision of feature tracking method; (3) The scale-space has heavily impact on the matching precision because the number of keypoints in multiple scale spaces is more than that of the keypoint detector in single scale space; (4) Superpixel-based segmentation can be used to find potentially keypoints that in the textureless regions. As a result, the matching precision of the SPFT is largely attributed to the usage of multiple scale spaces and superpixel segmentation; (5) Deep learning-based method, such as SuperPoint, can improve the matching precession in the single scale space of the image. Figure 4:0 Averaging matching precessions for the evaluated methods, where the SPFT has the best performance, the SuperPoint is in the second place. Figure 4:1 Matching results for the Colosseum dataset. #### 5.1.2 Computation Time

Computational cost is one of the most evaluation metrics for feature tracking methods, thus, we have collected the computation times for each compared feature tracking method according to the assessment that conducted on Colosseum dataset. The statistical results of computation times for each method are depicted in Figure 9 where the computation time is the sum of that spend on the whole pipeline including keypoint detection, descriptor computing, and feature matching. We can clearly see that the SPFT has the fastest speed, the averaging computation time is 6.7 s. The ENFT is in the second place, its averaging computation time is 9.2 s. Among those compared methods, the ROML has the lowest speed, which requires 21.3 s averagely for image pairs matching. After deeply investigation for ROML, we found that the main reason attributed to the highest computational cost of ROML is implementation in MATLAB routines. We hold that the ROML may be significantly accelerated when implementation in C++ programming language. As shown in Figure 9, the speed of the proposed SPFT is about 3 times faster than that of ROML, and is about 2-3 times faster than that of HPM and MODS. According to the statistical results of matching precision and computation time, we can conclude that the SPFT feature has the best performance in both accuracy and efficiency. In addition to ROML, the SuperPoint has the lowest speed, the averaging time is 18.2 s according to the experiment. ### Evaluation on HFUT Dataset

In the field of 3D reconstruction, if a feature tracking method is integrated into a 3D reconstruction system, which can produce high-quality point-cloud model, we consider the feature tracking method as a good approach to 3D reconstruction. Based on this judgement criteria, we create a new dataset captured by Canon EOS 550D camera, we named the new dataset as HFUT dataset for short. Figure 10 presents samples of the HFUT dataset, which contains 120 images and have many repeated features and repeated structures on the surface of each image. In addition to repeated features, the light for each image is very weak, which pose a new challenge for feature tracking method. In this experiment, we integrated the SPFT feature tracking method into ISFM system [2] to recover the point-cloud model, the results are shown in Figure 4:2 We can see that the reconstructed point-cloud model has highly geometric consistency with respect to the real scenario.