# KD-PatchMatch: A Self-Supervised Training Learning-Based PatchMatch
## 4 Experiments
### Experimental Results  
**Evaluation on DTU Dataset. ** We set \(N=5\) and input images resolution as \(1600\times 1200\). We use the DTU dataset's [9] three evaluation metrics, accuracy, completeness, and overall. As shown in Table 1, our method outperforms others in terms of completeness and overall quality, despite the fact that Gipuma [23] performs best in terms of accuracy. Figure 3 depicts a comparison of reconstructed point clouds. This result shows that our method achieves significantly higher reconstruction quality than PatchMatchNet [15] and other supervised methods, such as the roof reconstruction is more complete and provides more precise borders, and the logo letters have finer details. Since our method samples depth values for each pixel around the single-view probability distribution, multi-view matching may fail for texture-free  
Figure 3: Qualitative comparison of scan15 of DTU [9]: bottom, zoom in; our method preserves the integrity of the blue roof better than PatchMatchNet [15], with smoother results and finer detail in the logo letters. single-view which is more reliable, our method is 6.8% more effective in terms of completeness compared to PatchMatchNet [15]. In addition, our method introduces a self-supervised pipeline based on knowledge distillation, which transfers the deep pseudo-probability knowledge from the teacher model to the student model. After our experiments, the student model is better than the teacher model. Our method is 8.2% more effective in terms of accuracy compared to PatchMatchNet [15]. Combining the results in Table 1, the overall reconstruction effect of our method is 7.7% better than PatchMatchNet [15]. We visualize reconstructed point clouds from DTU's evaluation set [9] in Figure 4.  
**Evaluation on Tanks and Temples Dataset. ** We test our method on the Tanks and Temples benchmark [10] to demonstrate its generalization ability on different data. Without any fine-tuning, we employ the model trained on DTU [9]. In evaluation, we set \(N=7\) and image resolution as 1920 \(\times\) 1056. We recover the camera parameters and sparse point clouds using OpenMVG [42]. On intermediate datasets, our method performs similarly to CasMVSNet [12], as shown in Table 2. For reconstructing more difficult advanced datasets, our method outperforms the best in all of the methods. Overall, our method demonstrates competitive generalization performance. We visualize reconstructed point clouds from the Tanks and Temples dataset [10] in Figure 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Acc. (mm)** & **Comp. (mm)** & **Overall (mm)** \\ \hline Camp [39] & 0.835 & 0.554 & 0.695 \\ Furu [21] & 0.613 & 0.941 & 0.777 \\ Tola [40] & 0.342 & 1.190 & 0.766 \\ Gipuma [23] & **0.283** & 0.873 & 0.578 \\ SurfaceNet [38] & 0.450 & 1.040 & 0.745 \\ MVSNet [7] & 0.396 & 0.527 & 0.462 \\ R-MVSNet [8] & 0.383 & 0.452 & 0.417 \\ CIDER [6] & 0.417 & 0.437 & 0.427 \\ P-MVSNet [5] & 0.406 & 0.434 & 0.420 \\ Point-MVSNet [16] & 0.342 & 0.411 & 0.376 \\ Fast-MVSNet [41] & 0.336 & 0.403 & 0.370 \\ CasMVSNet [12] & 0.325 & 0.385 & 0.355 \\ UCS-Net [18] & 0.338 & 0.349 & 0.344 \\ CVP-MVSNet [14] & 0.296 & 0.406 & 0.351 \\ PatchMatchNet [15] & 0.427 & 0.277 & 0.352 \\ ours & 0.392 & **0.258** & **0.325** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative results of different methods on DTU’s evaluation set [9] (lower is better). Figure 4: Reconstruction results on a partial evaluation set of DTU [9]. **Evaluation on ETH3D Benchmark. ** Without any fine-tuning, we employ the model trained on DTU [9]. For evaluation, we set the size of the input image to \(2688\times 1792\). Because of the strong viewpoint variation in ETH3D [11], we also use a view with \(N=7\) to exploit more multi-view information. We recover camera parameters and sparse point clouds using COLMAP [24].