# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 5 Discussion  
Table 4 presents the comparative results of our model with other models, categorized into three types, supervised, generalized, and self-supervised models, which are, respectively, positioned at the top, middle, and bottom parts of the table. Unlike the generalized and self-supervised models, supervised models overlook the inaccuracy of point cloud labels in the _SCARED_ dataset, which is a theoretical flaw. Within the generalized and self-supervised categories, only our model and method [15] recognize the problem of imperfect image correction, resulting in misalignment of matching points in both the horizontal and vertical directions. Method [15] has designed a vertical correction module to address pixel shifts in the vertical direction, while our correction compensation module is designed to adjust errors in both the vertical and horizontal directions. Theoretically, image correction  
Figure 7: Subjective results on _SCARED_. The areas with noticeable differences are marked with dashed lines. Compared to PASMnet, our predictions are closer to the labels. Specifically, our predictions are smoother in textureless regions and avoid significant errors in specular highlight areas.  
is a homography transformation process. Thus, compensating errors simultaneously in both directions can effectively counteract the correction inaccuracies, allowing the network backbone to focus on the registration task and thereby achieving better predictive results. Additionally, only our model performs differentiated cost aggregation based on the values of cost volume elements. This trick is used considering that the cost volume is a collection of image features, with ill-posed characteristics such as specular highlights and textureless areas scattered throughout. The registration of elements in ill-posed areas is typically more challenging compared to other elements. By emphasizing their cost information during the aggregation process, we can improve the prediction accuracy of the model.  
We have demonstrated the effectiveness of the correction compensation module and the adaptive cost aggregation module through ablation studies (see Tables 2 and 3). Thanks to these modules, our model's predictions exhibit less noise in ill-posed areas, and are overall smoother and closer to the true values. The quantitative results in Tables 4-6 also confirm the efficacy of our proposed model, significantly enhancing the performance of the baseline model under imperfectly corrected stereo images.  
It is noteworthy that our model closely matches the performance of supervised models, with the significant advantage of not relying on labels during the training process. Collecting depth labels in laparoscopic surgery scenarios is a challenging task, and the training of supervised models is hindered. Our model offers an alternative solution. Furthermore, our designed ACAO can be conveniently ported to other models. Its input and output are both three-dimensional cost volumes. Therefore, it can be directly added after the cost aggregation unit. For networks aggregating three-dimensional cost volumes, no additional operations are needed. For networks aggregating four-dimensional cost volumes, only the dimensionality of the cost volume needs to be reduced beforehand through averaging or other operations. Therefore, the work presented in this paper holds the potential to improve the performance of stereo matching in other scenarios, such as autonomous driving.  
However, the disparity predictions of our model still exhibit progressive and blurry artifacts at the edges of the structures. Furthermore, under the experimental conditions described in this paper, the inference speed of the model does not yet meet the requirements for real-time performance. Our future research plan will focus on addressing these problems.