## 4 Experiments
### Ablation Analysis  
To verify the validity of the progression scheme proposed in this paper, the ablation test is designed according to each improvement module. This test uses the same equipment and self-built datasets from field samples for training and testing to ensure comparability, and the comparison graph with the base model test is shown in Figure 10. Where Figure 10(a1-a3,b1-b3 and c1-c3) shows the localized zoomed in view of the results generated by each model. The base model MVSNet's multi-view Figure 10(a1-a3), affected by the complex environment of the orchard, there is interference information in the trunks of the fruit trees, resulting in poor model integrity, and cannot well distinguish the main model structure. Comparing the trunk detail maps generated by MVSNet + U-Net feature extraction network Figure 10(b1-b3) with Figure 10(c1-c3) of MVSNet + Co U-Net + DRE-Net_SA method, it can be seen that b1 is more than c1 the trunk portion of the tree is missing incomplete and the transition is not natural; Figure 10(c2) has a better effect than Figure 10(b2) the elimination of the edge and the background noise. Effective segmentation of the background interference; Figure 10(c3) than Figure 10(b3) more effective extraction of image detail features, in the texture sparse region rich semantic information, the reconstruction of the object edge of the smoothness and completeness have been greatly improved. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Acc** & **Comp** & **Overall** \\ \hline Gipuma [34] & 0.283 & 0.873 & 0.578 \\ CAMP [18] & 0.835 & 0.554 & 0.695 \\ COLMAP [35] & 0.400 & 0.664 & 0.532 \\ MVSNet(baseline) & 0.396 & 0.527 & 0.462 \\ R-MVSNet [19] & 0.385 & 0.421 & 0.391 \\ D2 HC-MVSNet [20] & 0.395 & 0.378 & 0.386 \\ Point-MVSNet [36] & 0.361 & 0.421 & 0.391 \\ Cascade-MVSNet [37] & 0.325 & 0.385 & 0.355 \\ MVSNet + Co U-Net + DRE-Net_SA & 0.354 & 0.338 & 0.346 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of comparative test results. The data from the ablation test results are shown in Table 2, from which we can know by analysis. Firstly, the composite U-Net feature extraction network structure used in feature extraction is tested, and the feature extraction network with a single U-Net structure is compared based on the base model. From the first and second rows, the performance of the base model has been optimized in each index after adding the U-Net feature extraction network, which proves that the base model underperforms in the complex orchard environment and demonstrates the feasibility of the improvement direction of the feature extraction network. As can be seen from the third line, compared with the single U-Net feature extraction \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Acc** & **Comp** & **Overall** \\ \hline MVSNet(baseline) & 0.560 & 0.538 & 0.549 \\ MVSNet + U-Net & 0.502 & 0.486 & 0.494 \\ MVSNet + Co. U-Net & 0.486 & 0.492 & 0.489 \\ MVSNet + Co U-Net + DRE-Net\_SA & 0.446 & 0.469 & 0.457 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ablation test results. Figure 10: Comparison of reconstruction results of datasets by different models. network, the use of a composite U-Net feature extraction network can try the model in the convolution process more times, deeper depth, and better semantic segmentation of the original image of the orchard, so that the performance of Acc and Overall metrics has improved. However, the performance of the Comp metrics decreased, and we analyzed the reasons that led to the performance degradation:  
* Since the composite U-Net architecture is more efficient in processing specific features (e.g., edges, textures, etc. ), it is not good at capturing the full details of the scene, which affects the completeness of the model's reconstruction of the objects. * Composite U-Net may lead to a certain degree of overfitting because of its complex architecture and larger model capacity, especially when there is less data or insufficient diversity.