# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 4 Experiments
### Results  
The proposed method was subject to evaluation using the official evaluation metrics on the testing set of the DTU dataset. During the evaluation phase, five source images were employed at an input resolution of \(1600\times 1184\). As indicated in Table 1, a comparison of our method with several other stereo algorithms revealed a commendable level of accuracy and completeness. Specifically, our method exhibited competitive performance. The F score of our result ranks first among the comparison algorithms, and has high completeness and precision. This validates its legitimacy in the current experimental paradigm. Moreover, the reconstruction capabilities of our algorithm are showcased in Figure 10, where an accurate and detailed 3D model was generated. This outcome was largely attributable to the proximity of the depth information of each point in the model to the ground truth, as well as accurate pixel positioning within the images. The RGB value of each spatial 3D point was assigned its corresponding actual value, thus increasing the overall refinement of the model. Further evaluation of our method was conducted via a comparison with other learning-based methods, with a focus on runtime and GPU memory consumption. Table 2 effectively illustrates that the inclusion of normal estimation as an additional task did not exert significant pressure on either memory or time consumption. Compared with other algorithms, our time consumption ranks second, and while adding an additional task, our GPU memory consumption is not the highest among the comparison algorithms. Notably, several normal maps were generated during the experiment to showcase the algorithm's adroitness regarding the interpretation of surface structures, as illustrated in Figure 11. These outcomes confer further evidence of the aptitude of our method in capturing pertinent information essential for effective modeling. It is worthwhile to note that the resultant normal maps could be combined with the depth point cloud for further optimization and integration, which is an area that warrants further inquiry. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Acc. (mm)** & **Comp. (mm)** & **Fscore (mm)** \\ \hline Tola [32] & 0.342 & 1.190 & 0.766 \\ Gipurma [33] & **0.283** & 0.873 & 0.578 \\ Colmap [3] & 0.400 & 0.664 & 0.532 \\ CIDER [25] & 0.417 & 0.437 & 0.427 \\ P-MVSNet [26] & 0.406 & 0.434 & 0.420 \\ R-MVSNet [21] & 0.383 & 0.452 & 0.417 \\ D2 HC-RMVSNet [34] & 0.395 & 0.378 & 0.386 \\ Point-MVSNet [24] & 0.342 & 0.411 & 0.376 \\ Fast-MVSNet [27] & 0.336 & 0.403 & 0.370 \\ Vis-MVSNet [35] & 0.369 & 0.361 & 0.365 \\ CasMVSNet [15] & 0.325 & 0.385 & 0.355 \\ PatchmatchNet [18] & 0.427 & **0.277** & 0.352 \\ CVP-MVSNet [14] & 0.296 & 0.406 & 0.351 \\ Ours & 0.337 (5 th) & 0.356 (2 nd) & **0.346** (1 st) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of accuracy and completeness with other methods on the DTU dataset. To furnish conclusive evidence of the generalization capabilities of our method, the model was subject to extensive testing on the Tanks and Temples dataset. The evaluation was conducted in a comprehensive manner, with a focus on image resolution and the number of source images employed, which were 1080 \(\times\) 1920 and five, respectively. Table 3 displays the final F1 score comparison results, which effectively convey the generalization potential of our method. Additionally, some illustrative qualitative visualizations of the resultant models are depicted in Figure 12, highlighting the robust reconstructive abilities of our algorithm, particularly in the context of large-scale outdoor scenes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Mem. (GB)** & **Time. (s)** & **Normal** \\ \hline Vis-MVSNet [35] & 5.6 & 0.61 & NO \\ Fast-MVSNet [27] & 7.0 & 0.52 & NO \\ CVP-MVSNet [14] & 8.8 & 1.51 & NO \\ CasMVSNet [15] & 9.1 & 0.55 & NO \\ PatchmatchNet [18] & 3.6 & 0.25 & NO \\ Ours & 7.8 & 0.52 & YES \\ \hline \hline \end{tabular}
\end{table}