## 4 Experiments
### Benchmark Performance  
#### 4.3.1 Evaluation on DTU Dataset  
In this section, we compare the performance of our model with traditional methods, learning-based methods, and the methods reported in the latest technical literature.  
To better analyze the differences between various methods, our approach is compared with Gipuma [4], Effi-MVSnet [43], DA-PatchmatchNe [44], and CasMVSNet [6]. Among these, Gipuma employs a disparitypropagation strategy from traditional 3D reconstruction methods, proposing a diffusion propagation strategy utilizing GPU's multicore architecture for multi-view 3D reconstruction. Effi-MVSnet utilizes GRU based on 2D convolution to generate cost volumes. DA-PatchmatchNe combines data augmentation with traditional multi-scale patchmatchalgorithm. Cas-MVSNet adopts a cascaded approach to construct cost volumes, gradually refining the depth sampling range from coarse to fine, ensuring overall efficiency of reconstruction and rational allocation of computational resources.  
We opt for input image resolutions of 1600 \(\times\) 1200 with the number of views set at \(N=5\). Employing the official evaluation metrics provided by the DTU dataset, we compute reconstruction accuracy (Acc.), completeness (Comp.), and their average, termed overall error (Overall), measuring the reconstruction errors between the generated point cloud and the ground truth. Smaller values of these three metrics indicate better reconstruction performance.  
As shown in Table 1, Gipuma [4] achieves the highest score of 0.283 in the Acc. metric, indicating superior performance. While DA-PatchmatchNetbi [44] excels in the Comp. metric with a score of 0.272, our proposed method also achieves impressive results, scoring 0.279, with only a marginal difference of 0.007. Notably, our Overall metric stands at 0.315, leading among all methods. In comparison to the mainstream CasMVSNet [6], we achieve a relative improvement of 11.27%.  
Furthermore, Figure 6 illustrates a comparison of the reconstruction results between our method and CasMVSNet [6] in different scenes. The results indicate that the reconstruction outcomes of this paper are denser and exhibit finer details, successfully alleviating the impact of noise in local areas. It is noteworthy that in regions with weak textures, our method preserves more details. This is primarily attributed to the uncertainty dynamic range sampling module, which comprehensively covers real-depth values. Additionally, the introduced cross-scale cost volume information exchange module better handles information connections between different dimensions and scales, further enhancing information utilization. In summary, our proposed algorithm outperforms other methods, showcasing superior competitiveness in terms of reconstruction quality.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Acc. (mm)** & **Comp. (mm)** & **Overall (mm)** \\ \hline Gipuma [4] & **0.283** & 0.873 & 0.578 \\ Tola [3] & 0.342 & 1.190 & 0.766 \\ COLMAP [45] & 0.400 & 0.664 & 0.532 \\ MVSNet [5] & 0.396 & 0.527 & 0.462 \\ CIDER [34] & 0.417 & 0.437 & 0.355 \\ CVP-MVSNet [46] & 0.296 & 0.406 & 0.351 \\ R-MVSNet [25] & 0.383 & 0.452 & 0.417 \\ Fast-MVSNet [27] & 0.336 & 0.403 & 0.370 \\ CasMVSNet [6] & 0.325 & 0.385 & 0.355 \\ Point-MVSNet [33] & 0.342 & 0.411 & 0.376 \\ Epp-mvsnet [23] & 0.413 & 0.296 & 0.355 \\ AA-RMVSNet [8] & 0.376 & 0.339 & 0.357 \\ UCS-Net [28] & 0.338 & 0.349 & 0.344 \\ Effi-MVSnet [43] & 0.321 & 0.313 & 0.317 \\ DA-PatchmatchNet [44] & 0.417 & **0.272** & 0.344 \\ Ours & 0.351 & 0.279 & **0.315** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of different methods on the DTU evaluation set (lower values are better). The best and second-best results are highlighted in bold and underlined, respectively.  
Figure 6: DTU reconstruction results’ comparison. (**a**) Ground Truth. (**b**) CasMVSNet reconstruction results. (**c**) The reconstruction result of our algorithm.