# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 4 Experiments and Results
### Comparison Experiments  
We compared our model with stereo matching methods reported in the MICCAI SCARED sub-challenge using the official evaluation code from [14]. The results are shown in the upper section of Table 4, where the data represent the average errors of the estimated depth. We only listed the top-ranked works, all of which were trained in a supervised manner. Our model achieved a fourth place performance and was in close proximity to that of the top-ranked model. Furthermore, we conducted comparisons between our model and several state-of-the-art models that were well-generalized or unsupervised. The results are presented in the middle and lower sections of Table 4, respectively. The authors of these models also noted the scarcity of labeled laparoscopic datasets. However, our solution surpassed theirs in terms of quantitative performance, as evidenced by the evaluation results. Considering the labels of subsequent frames in the test videos were inaccurate except for the first frame, we evaluated our model only on keyframes. The left and right 100 pixels were cropped due to invalidity after correction, and pixels with a labeled disparity over 192 were excluded. The results are shown in Table 5; our model shows an improvement compared with HybridStereoNet, which achieved state-of-the-art performance in generalization ability. Our model also surpasses the benchmark PASMnet, which is a state-of-the-art unsupervised method for autonomous driving data. Furthermore, in order to enhance the statistical significance of the evaluation results and simultaneously mitigate the interference from inaccurate labels, we computed the SSIM metric on the original left images and reconstructed left images across all images in the _SCARED_ test set. The results are presented in Table 6 and reflect the superiority of our model. Figure 6: An illustration of the descending curves of (**a**) the total loss and the (**b**) PAM loss during the training process. The PAM loss highly depends on the aggregated cost information. However, with the inclusion of ACAO, both curves exhibit a faster decline. We can infer that this operation facilitates the network in capturing high-value information from the cost matrix while filtering out low-value information, thereby accelerating the convergence of the model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**ACAO** & **IGCS** & **DCS** & **GCS** & **Params (G)\(\downarrow\)** & **Flops (M)\(\downarrow\)** & **EPE\(\downarrow\)** & **D1\(\downarrow\)** & **D3\(\downarrow\)** & **MAE\(\downarrow\)** \\ \hline  & & & & 7.82 & 75.46 & 1.951 & 12.073 & 9.557 & 1.897 \\ ✓ & & & & 7.82 & 75.46 & 1.873 & 10.367 & 8.314 & 1.799 \\ ✓ & ✓ & & & 6.22 & 52.83 & 1.877 & 10.767 & 8.540 & 1.904 \\ ✓ & ✓ & ✓ & & 6.42 & 55.66 & **1.848** & **10.300** & **8.203** & **1.777** \\ ✓ & & ✓ & ✓ & 6.42 & 55.66 & 1.952 & 12.035 & 9.566 & 1.897 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation results for the adaptive cost aggregation module. It reduces the parameter number by 18% and the floating-point operations number by 26%, while improving the model’s EPE and MAE metrics by over 5%, as well as the D1 and D3 metrics by over 14%. The number of floating-point operations was calculated through an input matrix of size \(672\times 672\times 3\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **SSIM \(\uparrow\)** \\ \hline HybridStereoNet [10] & 62.38 \\ PASMenet [21] & 62.22 \\ Ours & **67.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation results of the SSIM metric for all test images. Our model shows an improvement over the baseline model and HybridStereoNet.