# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 2 Related Work
### Learning-Based Stereo Matching  
Recently, learning-based stereo matching networks have achieved state-of-the-art performance. [PERSON] [22] proposed the first end-to-end stereo matching network, DispNet, and its correlation version DispNetC. [PERSON] et al. [23] introduced a novel approach in which they formulated a cost volume by concatenating the features extracted from an image pair and subsequently aggregated it through the utilization of 3D convolutions. Subsequent work primarily builds upon these two frameworks. For better performance in ill-posed image areas such as textureless surfaces, specular highlights, and occlusions, [PERSON] et al. [5] proposed a pyramid stereo matching network that incorporated a spatial pyramid pooling module to expand the receptive fields and capture more representative features, which were then aggregated using stacked 3D hourglass networks. [PERSON] et al. [24] made traditional semi-global and local matching algorithms differentiable and applied them in the cost aggregation phase. [PERSON] et al. [9] treated the correlation cost volume as an attention module for the concatenation cost volume, guiding the concatenation operation to retain more information useful for registration, and introduced a multi-level adaptive patch matching module to improve the distinctiveness of the matching cost at different disparities even for textureless regions. [PERSON] et al. [25] proposed the local similarity pattern to learn the relationships among weights within a convolutional kernel, which reflects the local structural property. The authors also designed a dynamic self-reassembling refinement strategy and applied it to the cost distribution and the disparity map, respectively. This strategy allowed the elements within the cost volume and disparity map to fuse information from a limited number of neighboring elements, and enabled better information aggregation for isolated elements near occlusions. To address the challenge of acquiring accurate depth labels in special scenarios, such as surgical environments, researchers have advocated for self-supervised stereo matching approaches. [PERSON] et al. [20] employed a left-right consistency check to generate confidence maps, thereby guiding the training of networks. [PERSON] et al. [26] conducted semantic feature embedding and regularized semantic cues as the loss term to improve learning disparity. [PERSON] et al. [27] proposed a stereo matching network utilizing occlusion information, where the authors introduced an occlusion inference module to provide occlusion cues. Additionally, they proposed a hybrid loss that leverages the interaction between disparity and occlusion to guide the training process. [PERSON] et al. [21] introduced a parallax attention mechanism (PAM) and used it to construct the self-supervised stereo matching network, PASMnet. The inherent left-right consistency and cyclic consistency of PAM acts as additional guidance for model training, and PAM efficiently calculates the feature correlation between any two positions along the epipolar line, which helps to avoid the necessity for the network to set a fixed maximum disparity. To address the problem of correction errors present in real-world datasets, [PERSON] et al. [12] proposed an adaptive group correlation layer that allows for offsets in the feature elements participating in correlation calculations. This design avoided the exclusion of misaligned features caused by correction errors during the feature similarity computation. The adaptive group correlation layer effectively copes with stereo image pairs that are insufficiently rectified, offering a solution for the application of stereo matching algorithms on consumer-grade devices. However, the authors simulate correction errors using data augmentation techniques such as random homography transformation on synthetic datasets, which differ from real image correction errors. This discrepancy somewhat limits the model's ability to capture true image correction errors in real scenarios.