# Hash Indexing-Based Image Matching for 3D Reconstruction
## 4 Experimental Results
### Evaluation on the Open-Source Dataset  
To verify the effectiveness of the HashMatch in 3D reconstruction, we integrated it into the Bundler and evaluated it by using the open-source dataset ([[https://github.com/report/ReconstructionDataSet](https://github.com/report/ReconstructionDataSet)]([https://github.com/report/ReconstructionDataSet](https://github.com/report/ReconstructionDataSet)), accessed on 12 April 2021). It should be noted that this dataset contains nine sequences. In our experiment, the \"AvignonHotelDesMonnaies\" (in the following section, we call it a \"Hotel\" for convenience) was adopted because it is the most challenging sequence. Figure 10 presents the samples of the Hotel sequence, there are many repeated features on the surfaces of the sampling images, and this raises a big challenge for the image matching method. The matching results of the compared methods are depicted in Figure 11, where it can be seen that the BFM has the most incorrect feature correspondences, meaning that BFM had the lowest matching precision among these image  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Quantiative Analysis**} & \multicolumn{8}{c}{**Method**} \\ \cline{2-9}  & **BRSK + HM** & **ORB + HM** & **AKAZE + HM** & **KAZE + HM** & **SIFT + HM** & **SURF + HM** & **SURF + VGG** & **SIFT + VGG** \\ \hline Features & 19,808 & 20,000 & 11,896 & 11,434 & 38,991 & 45,944 & 45,944 & 38,991 \\ Matches & 3037 & 3857 & 4571 & 4699 & 6437 & 8539 & 7945 & 4808 \\ Ratio of Inliers & 79.34\% & 81.14\% & 88.56\% & 89.67 & 91.56 & 97.72 & 89.76 & 84.62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results (correct matches) of the Semper sequence of [PERSON]’s dataset, where HM represents HashMatch, “SURF + VGG” represents SURF detector and VGG descriptor, and “SIFT + VGG” represents SIFT detector and VGG descriptor.  
Figure 8: The calculation time of each method. According to these statistical results, the proposed method, HashMatch, has the fastest speed.  
matching methods. Although the RatioTest can remove some outliers, there are also many incorrect feature correspondences in the collection of matches. The ENFT obtained a better result than that of the KNN + RatioTest because of its use of the two-pass matching strategy. The HashMatch yielded the best results among these methods as there were no outliers in the collection of feature correspondences.  
Figure 10: Samples of the Hotel sequence. Note that these figures have more repeated features and symmetrical structures.  
Figure 9: Feature matches of the Semper sequence, where the combination of “SURF + HashMatch” has the highest number of correct feature correspondences. Again, this proves the validity of the HashMatch method.  
Three ways are adopted to evaluate the performance of the HashMatch in 3D reconstruction, namely sparse reconstruction by Bundler, dense reconstruction by the PMVS, and surface reconstruction via the Poisson surface reconstruction. The sparse point clouds, dense point clouds, and textured models are provided in Figures 12-14, respectively. In each stage, the reconstructed model had a high geometry consistency with the real scene. Moreover, the HashMatch to one minute to generate feature correspondences. To this end, all of our results (Figures 11-14) prove the effectiveness of the HashMatch in 3D reconstruction.