## 3 Results and Discussion
### Quantitative Evaluation and Comparison
#### 3.2.2 Comparison with Other MVS Algorithms
In contrast, as can be seen from enlarged views in the second and third columns, the 3D point clouds reconstructed by the DAISY algorithm has some hollow places; the 3D point clouds reconstructed by the PMVS algorithm contained many discontinuous warped pieces; the 3D point clouds reconstructed by MRF algorithm were too dense to provide good accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & **Mean Complete** & **Mean Accuracy** & **Med Complete** & **Med Accuracy** & **Point Number** \\ \hline DAISY & 0.54 & 0.28 & 0.40 & 0.20 & 1,000,782 \\ PMVS & 0.45 & 0.37 & 0.37 & 0.25 & 2,490,988 \\ MRF & 0.17 & 0.63 & 0.15 & 0.41 & 21,918,711 \\ Proposed & 0.41 & 0.28 & 0.33 & 0.19 & 2,029,048 \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation results (mm) for _house model_ by different MVS algorithms. Figure 10: The ground truth and reconstructed 3D point clouds of _volleyball_. From top row to bottom row: ground truth, reconstructed 3D points by DAISY, PMVS, MRF, and the proposed algorithm. The second column shows enlarged views of the corresponding green rectangles in the same row of the first column, whereas the third column shows enlarged views of the corresponding red rectangles in the same row of the second column. The quantitative evaluation results for _volleyball_ are listed in Table 5. The evaluation was performed for the same volumes, manually cut out from the ground truth and all reconstructed dense points where the ground truth was complete (the regions in the red rectangles in the first column in Figure 10). From Table 5, it can be seen that the mean accuracy of the reconstructed 3D point cloud by the proposed algorithm, 0.28 mm, was very close to the highest, 0.27 mm by the DAISY algorithm and the mean complete of the proposed algorithm, 0.99 mm, was much better than the 2.19 mm of the DAISY algorithm and the 1.52 mm of the PMVS algorithm. The number of 3D points, 7,451,571, produced by the MRF algorithm was much higher than the number, 4,444,883, of the ground truth. Due to this redundancy, the completeness of the MRF algorithm was the highest, although its accuracy was low. The number of 3D points produced by DAISY, PMVS and the proposed algorithm were 469,749, 1,383,713 and 1,356,621, respectively. As can be seen from the enlarged views of reconstructed 3D point clouds, our algorithm provided fine reconstruction quality. According to the quantitative evaluation results, the DAISY algorithm attained the highest accuracy at the cost of a reduced completeness. We guess that the reason for it producing the smallest number of 3D points is that it keeps only reliable 3D points. These points are usually at regions with enough texture. In contrast, the MRF algorithm generated the highest completeness at the cost of a much-reduced accuracy. We guess that the reason for it producing the largest number of 3D points is that it integrates the depths without careful selection. In addition, it uses NCC for window-based matching, which is inferior to the DAISY descriptor. We extended the framework of feature expansion algorithm by an additional step for initial value modification and made important improvements to other existing steps, which is why our algorithm outperforms PMVS in terms of both accuracy and completeness. We demonstrated that through feature expansion, many places with weak texture might be reconstructed with high accuracy. The accuracy averaged over the entire reconstructed area is still on par with the highest one of Tola.