Eglcr: Edge Structure Guidance and Scale Adaptive Attention for Iterative Stereo Matching

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-07 17:41:42

Abstract: Stereo matching is a pivotal technique for depth estimation and has been popularly applied in various computer vision tasks. Although many related methods have been reported recently, they still face some challenges such as significant disparity variations at object boundaries, difficult prediction at large disparity regions, and suboptimal generalization when label distribution varies between source and target domains. Therefore, we propose a stereo-matching model (i.e., EGLCR-Stereo) that utilizes edge structure information with adaptive fusion of multi-scale matching similarity information for disparity estimation. First, we use a lightweight network to predict the initial disparity. We apply large and small-scale similarity feature extraction modules to extract the matching similarity information within the wide-area receptive field and the refined matching similarity information under the local receptive field. Then, we develop a scale adaptive attention module for efficiently fusing information at different scales. Meanwhile, we propose an edge structure-aware module for exploring edge information in the scene. After that, we use an iterative-based strategy for disparity estimation using edge structure information with fused multi-scale matching similarity information. We conduct abundant experiments on some popular stereo matching datasets including Middlebury, KITTI, ETH3D, and Scene Flow. The results show that our proposed EGLCR-Stereo achieves state-of-the-art performance both in accuracy and generalization.

Main paper: 
 introduction: 
 3D scene information plays a crucial role in environmental perception, and estimation of 3D scene information attracts extensive attention in many computer vision fields, such as robotic navigation, autonomous driving, and intelligent industrial monitoring [7, 11, 19] , and so on. Actually, the estimation of 3D scene information always uses computer vision to calculate depth information from single, double or multi-view images. The estimation method can be mainly divided into monocular depth estimation, stereo matching, and multi-view depth estimation. Among these methods, stereo matching method is the most popularly used and researched one by the scholars and engineers, because it has simple acquisition system structure and high computational efficiency [1, 6, 8, 20] .Stereo matching method can be broadly categorized into traditional method and deep learning method. The traditional method requires to manually design the calculation strategy and the deep learning method uses the deep neural network to estimate disparity in an end-to-end manner [10, 12, 13, 41] . Compared to the traditional method, the deep learning method has better computational efficiency, robust generalization capabilities and enhanced performance. Therefore, it has gradually dominated the field of stereo ). In the areas with large disparity, our method achieves smoother and more accurate disparity (see 𝑐 1 and 𝑐 2 ). For cross-domain prediction, our model also outperforms RAFT-Stereo at the structural edges of objects (see 𝑑 1 and 𝑑 2 ).matching. One famous deep learning model for stereo matching is PSMNet [1] , which is the representative of volume-based stereo matching models. This kind of model employs matching feature maps to construct a cost volume, and then regularizes the cost volume by some strategies to obtain the disparity map. As it uses the stacked hourglass 3D CNN to extend the regional support of context information in cost volume, it shows good performance on stereo matching. However, the volume-based method has substantial memory consumption and it may lead to a suboptimal detail prediction during cost volume regularization. To overcome this problem, another famous model for stereo matching, RAFT-Stereo [16] , is proposed. It iteratively refines the disparity predictions by the contextual information to predict disparity residuals based on current disparity results, and thus obtain more detailed and precise disparity results. This kind of model using iterative refinement strategy is always called as the iterative-based stereo matching method.Recently, an increasing number of iterative-based methods have been proposed and have achieved state-of-the-art (SOTA) performance on stereo matching. However, it still faces some challenges such as significant disparity variations at object boundaries, difficult prediction at large disparity regions, and decreased generalization when domains gap exists between the source and target domains. For example in Fig. 1(c ), there are some details missing at the edges of wheel spokes (a2) and plant's periphery (b2); some inaccurate and uneven disparities appear in the large disparity area and weak texture area on the computer screen (c2); average values at the basket edges (d2) is tend to be predicted without enough details during cross-domain prediction. There are some possible reasons for these unsatisfactory results. (1) The first reason is that during the iterative optimization process, the matching similarity features at larger scales are not effectively integrated with those at smaller scales. This often causes models to become trapped in local optima, leading to suboptimal predictions in areas with pronounced disparities.(2) The second issue is the inefficient perception of edge structure information throughout the iterative process. This inefficiency can lead to erroneous estimations and the omission of critical details along the edges of complex structures.Therefore, in this study, we present a stereo matching model designated as EGLCR-Stereo, which leverages multi-scale matching similarity features alongside refined edge structure information for disparity estimation. The proposed model adeptly addresses the aforementioned challenges and consistently delivers satisfactory outcomes. (Please see Fig. 1(b ) for example). The main contributions are as follows.(1) We develop a multi-scale similarity feature extraction module designed to capture the fusion similarity information of stereo images across various scales. This module encompasses a small-scale matching similarity feature extraction component employing group correlation, and a large-scale counterpart utilizing inner product operations to generate multi-scale similarity features. Notably, we introduce a scale-adaptive attention mechanism that dynamically adjusts the weights of large and small-scale matching similarity features based on the current matching state. This innovation significantly enhances the convergence speed of the iterative disparity refinement process.(2) We introduce an edge structure-aware module that incorporates an iteratively optimized disparity map and a scale attention factor. In this module, we design a disparity head that processes the iteratively optimized disparity and a texture head that handles texture information. Additionally, we use the scale attention factor from the MSFEAA module to fuse these elements, and then design an edge head for refined edge structures.(3) We employ a recurrent neural network to continuously predict residual disparities by integrating multi-scale matching similarity features with refined edge structures. Our model surpasses the baseline model on SceneFlow, Middlebury and ETH3D datasets, achieving state-of-the-art (SOTA) performance both on accuracy and cross-domain generalization. Our code will be open-sourced on Github. 
related works: 
 Recently, deep learning methods have been dominated the field of stereo matching and achieved impressive performance on most of standard benchmarks. These methods are mostly improved from the perspectives of cost volume aggregation, iterative disparity map optimization, and feature-guided stereo matching methods.Volume-aggregation based Method. To enhance the information fusion from left and right views and further improve the performance of stereo matching, many researchers [1, 10, 15, 31, 32, 35, 40] consider the improvements from feature extraction and cost volume aggregation. These methods always include four steps: feature extraction, cost volume construction, cost volume aggregation, and disparity map calculation process. For example, Liu et al. [17] employed a local similarity pattern to enhance the matching feature before the cost volume construction, and they reported that their model could effectively extract local structural information and improve the matching performance in the regions with edge structures. In the matching cost volume construction, Xu et al. [33] employed a fusion cost volume derived from the amalgamation of the groupwise cost volume and the concatenated cost volume to optimally assimilate the global and local information, and then regularize this cost volume to obtain disparity map. Although the above volumeaggregation based methods improve the matching performance, they still incur large calculations of cost volume regularization and the prediction errors in some complex areas.Iterative-based Method. To overcome the above limitations of volume-aggregation based method, some researchers [16, 29] introduced the iterative optimization strategy for stereo matching. This kind of method gradually generates disparity maps from coarse to fine. For example, RAFT-Stereo [16] employed a Gated Recurrent Unit (GRU) that is embedded in Convolutional Neural Network (CNN) for disparity map refinement, and improved the disparity prediction and inference speed. CREStereo [14] took the iterative optimization strategy and introduced an adaptive group correlation layer to alleviate the impact of imperfect rectification. They showed that their proposed model effectively improved depth estimation performance in cases where left and right view corrections are imperfect. In order to improve the model's understanding of contextual information, IGEV-Stereo [34] utilized the principles of residual learning and iterative enhancement and proposed a geometric encoding volume to continuously optimize the disparity map. By adopting an iterative optimization strategy, the iterativebased method can obtain more precise disparity prediction results than the volume-aggregation based method.Feature-guided based method. Beyond the methods previously discussed, several techniques [2, 9, 39] supervise disparity generation using supplementary feature information. Edge Stereo [27] integrated a dedicated edge prediction branch for predicting object edges and disparity related to the left view, and guided edge graph generation with edge-aware perception smoothness loss. Dai et al. [4] used the color and gradient consistency between the corresponding pixels in the view as supervisory signals to guide the generation of disparity maps. Xu et al. [37] used semantic information from corresponding pixels as supervisory signals to establish a semantic consistency loss function for model training, and enhanced the robustness of the model. It is crucial to use additional information to supervise the disparity generation in specific domains, but model performance depends on the effectiveness of the information.In this article, we propose an EGLCR-Stereo model using residual learning and multitasking learning principles. It effectively utilizes the multi-scale similarity and edge structure information to iteratively refines the disparity predictions from coarse to fine. 
eglcr-stereo: 
 Fig. 2 shows the network structure of the proposed EGLCR-Stereo model. It includes initial disparity estimation and disparity refinement stage, where the disparity refinement is our major contribution.Giving the left and right input images 𝐼 𝑙 , 𝐼 𝑟 ∈ R 3×𝐻 ×𝑊 , we adopt a MobileNet [22] pre-trained on ImageNet [5] to extract the feature maps 𝑓 𝑙 , 𝑓 𝑟 ∈ R 𝐶 × 𝐻 4 × 𝑊 4 . Then, we use a channel fusion module with a convolution kernel of 1 to enhance 𝑓 𝑙 , 𝑓 𝑟 , and then obtain the left and right enhanced matching features 𝐹 𝑙 and 𝐹 𝑟 . After that, we use 𝐹 𝑙 and 𝐹 𝑟 to construct a group-wise correlation cost volume 𝑉 and regularize it by a lightweight 3D convolution module to get the probability volume𝑉 ∈ R 𝑑 × ℎ 4 × 𝑤 4 .The initial disparity map is calculated by the expectation of the probability volume along the disparity dimension:EQUATIONwhere𝑑 (0) ∈ R ℎ 4 × 𝑤 4 .We design a multi-scale similarity feature extraction module with an adaptive attention mechanism, including a large-scale matching similarity feature extraction module, a small-scale matching similarity feature extraction module, and a scale adaptive attention module, as shown in Fig. 4 (a). Small-scale matching similarity feature extraction module. To refine the disparity result, we search the optimal disparity within a local neighbourhood for each candidate match point. We use a small-scale matching similarity feature extraction module (shown in Fig. 3(b) ) to extract the features of the neighbouring pixels. For the standard pixel coordinates (𝑖, 𝑗) of the left image and the initial disparity 𝑑 (0) (𝑖, 𝑗), the coordinate position of the matching point in the right image can be determined asEQUATIONAfter that, we sample the corresponding feature of 𝐹 𝑟 along the disparity direction within the range 𝛿. Then, we concatenate the reference feature 𝐹 𝑙 (𝑖, 𝑗) with the feature sampled from the right view to obtain the local similarity feature 𝑓 𝑙𝑜𝑐 (𝑖, 𝑗). The above process can be expressed by the formula:EQUATIONwhere N 𝑙 = { 𝑗 -𝑑 (𝑘 ) (𝑖, 𝑗) -𝛿, ..., 𝑗 -𝑑 (𝑘 ) (𝑖, 𝑗) + 𝛿 }, and Corr 𝑔 represents group-wise correlation calculation. Computing the similarity between matching features in high-dimensional space in the form of group correlation can characterize the similarity of matching feature vectors at different groups (e.g., color, texture, etc.) and improve the robustness of matching results, which can provide more information for subsequent disparity refinement.Large-scale matching similarity feature extraction module. It is insufficient to utilize only small-scale matching similarity feature for disparity refinement. This is because that if a substantial deviation occurs between the disparity in the current stage with the ground truth, the local contextual information will restrict the scope to obtain the global optimization. Therefore, we apply the large-scale perception module for a large receptive field, and help the model escape from the local solution and obtain the global optimal solution. Following by Raft-Stereo, we select the all-pair similarity measure as the global perception feature to guide the iterative refinement process. As shown in Fig. 3 (a), given the left and right feature maps 𝐹 𝑙 (𝑟 ) and disparity map 𝑑 (𝑘 ) , we sample the similarity along the initial disparity direction within the neighborhoods to obtain the global feature map:EQUATIONwhere 4 , N 𝑔 = { 𝑗 -𝑑 (𝑘 ) (𝑖, 𝑗) -8𝛿, ..., 𝑗 -𝑑 (𝑘 ) (𝑖, 𝑗) + 8𝛿 } indicates that the large-scale matching similarity feature extraction module considers a wider neighborhood information than that in the small-scale sampling module, Inner⟨•, •⟩ denotes the inner product operation that not only enhances computational efficiency but also optimizes memory utilization, compared to the group correlation operation. To further expand the reception field, we downsample the similarity matrices of the left and right feature maps along the disparity channel to construct a pyramid of similarity feature maps, and then neighborhood sampling operations are performed at each scale to obtain large-scale matching similarity features, as shown in Fig. 3 (a) .𝑓 𝑔𝑙𝑜 ∈ R N 𝑔 × ℎ 4 × 𝑤Scale adaptive attention module. When the predicted disparity is close to the global optimal value, small-scale matching similarity feature is more important than the large-scale matching similarity features. Conversely, when the predicted disparity is far from the global optimal value, the large-scale features should be more carefully treated. Therefore, we introduce a scale adaptive attention module to integrate the large and small scale matching information. As shown in Fig. 4 (b), We calculate a scale attention i.e., whereEQUATION𝑊 𝑠 ∈ R 3× ℎ 4 × 𝑤 4 = [𝑤 𝑙 , 𝑤 𝑔 , 𝑤 𝑜 ].Note that 𝑤 𝑙 , 𝑤 𝑔 , and 𝑤 𝑜 represents the weights of small-scale information, large-scale information, and the likelihood of a point being an outlier, respectively. After that, we weight the small and large scale matching similarity features by 𝑤 𝑙 and 𝑤 𝑔 , and then concatenate them along the feature channel to obtain the multi-scale fusing context feature 𝑓 𝑐𝑜𝑛 as:EQUATIONIn this module, we use both texture and disparity feature to estimate the edge map of the left view as shown in Fig. 5 . Given the substantial semantic information contained in the left feature map, we utilize 𝑓 𝑙 by a texture head to extract the texture feature of the left image. Then, we use a disparity head to extract disparity features on the disparity map at each iteration. After that, we use a edge head to calculate the edge map by integrating the texture and disparity feature. The above processes can be indicated as: where F e includes several convolutional layers and each convolutional layer is accompanied by a normalization and activation layer; Meanwhile, we implement an adaptive weighting strategy to maximize the efficacy of the disparity and texture branch. We introduce a parameter 𝜎 to incrementally amplify the impact of disparity branch throughout the iterative procedure as 𝜎 = 𝑘 𝐾+1 , where 𝑘 and 𝐾 denote the current iteration number and total iteration number, respectively. As the number of iterations increases, the weight of the disparity branch gradually increases to reduce the impact of inaccurate disparity maps during early iterations.EQUATIONThe iterative optimization considers the disparity and edge structure feature from the previous iteration, as well as multi-scale matching similarity information. To extract high frequency features from disparity and edge map as:EQUATIONwhere𝑓 (𝑘 ) 𝑑 ∈ R 𝑐 𝑑 × ℎ 4 × 𝑤 4 and 𝑓 (𝑘 ) 𝑒 ∈ R 𝑐 𝑒 × ℎ 4 × 𝑤 4 .After that, we combine high frequency features, matching similarity feature to update the hidden layer state of GRU and calculate the residual value of the disparity △𝑑 at each iteration. The process at the 𝑘 𝑡ℎ iteration can be indicated as:EQUATION)Loss function. The loss function includes edge loss L 𝑒 and disparity loss L 𝑑 . Considering the quantity imbalance between the pixel points in edge and non-edge regions, we use the weighted binary cross-entropy loss to represent the edge loss:EQUATIONwhere 𝑒 (𝑘 ) is the edge map predicted at the 𝑘 𝑡ℎ iteration, 𝑤 is the ratio of the number of edge pixels to non-edge pixels in the image, 𝑒 𝑔𝑡 is the ground truth of edge map and 𝛾 𝐾 -1-𝑘 is the weight at each iteration. Besides, we use 𝐿 1 loss and smooth 𝐿 1 loss to represent the disparity loss L 𝑑 as:EQUATIONFigure 6 : The first column denotes the original RGB images, the second column denotes the edge maps predicted by our adaptive weighted edge estimation module, the third column denotes the edge maps without disparity encoding features, the fourth column denotes the edge maps predicted by the HED 𝛽 method, and the fifth column denotes the edge maps predicted by EdgeStereo. Our model obtains more accurate boundaries of the real object than other models.Therefore, the total loss can be indicated as:EQUATION)where 𝜆 is a weight factor to balance the edge loss and disparity loss. 
experiment 4.1 dataset and evaluation metrics: 
 Scene Flow [20] The proposed EGLCR-Stereo is implemented by the PyTorch framework and trained on a server equipped with multiple 3090 graphics cards. During the first stage of training, we employ the AdamW optimizer [18] with an initial learning rate of 2e-4 and use the One-Cycle learning rate update strategy. The model is initially trained for 200k steps on the SceneFlow dataset. Then, the weight parameters related to edge prediction are frozen and only the parameters for disparity prediction are fine-tuned for an additional 100k steps. The input image size is randomly cropped to 320 × 736. On the Middlebury datasets, we use an initial learning rate of 1e-5, and freeze the weight parameters related to edge prediction and finetune the model for 10K steps with only disparity loss. The number of iterations for GRU disparity refinement is set to 20 and 32 during 2024-04-13 10:42. Page 5 of 1-9. We conducted the ablation studies on the edge structure-aware module, small-scale matching similarity feature extraction module and large-scale matching similarity feature extraction module.Edge structure-aware module. To demonstrate the effectiveness of the edge structure-aware module, we firstly compared the RAFT models with/without edge structure-aware module for disparity estimation. The results are shown in Tab. 1 (a) and (b). The 1-pixel error rate (disparity<192) decreases from 7.29 to 6.07 by utilizing the edge structure-aware module, which shows that the edge structure-aware module can increase the performance of disparity estimation. Besides, as we import the disparity information in our edge structure-aware module that is a main difference to other edge estimation modules (i.e., HED 𝛽 and EdgeStereo),we compared the EGLCR with/without disparity branch, as well as HED 𝛽 and EdgeStereo. The results are shown in Fig. 6 , and indicate that our model obtains more accurate boundaries of the real object than other models. Small-scale matching similarity feature extraction module. We compared the RAFT models with/without the small-scale matching similarity feature extraction module for disparity estimation. The results are shown in Tab. 1 (b) and (c). The integration of small-scale matching similarity feature leads to a reduction in 1-pixel error rate (disparity<192) metric from 6.07 to 5.71 and End Point Error (EPE:192) metric decreased by 7%, that is because the small-scale matching similarity feature could provide more detailed information for disparity refinement.Large-scale matching similarity feature extraction module. We compared the RAFT models with/without the large-scale matching similarity feature extraction module for disparity estimation. The results are shown in Tab. 1 (c) and (d). The integration of largescale matching similarity feature leads to a reduction in the 1-pixel error rate (all disparity) metric by 11.9%, that is because the largescale matching similarity feature can enhance the global perception ability of model and yield more resilient predictive outcomes in the areas with large disparity. On the MiddleBury dataset, our method outperforms other methods on multiple metrics. At the time of writing, our EGLCR-Stereo achieves rank 1 𝑠𝑡 on the avgerr (average absolute error in pixels) and A99 (99-percent error quantile in pixels) metrics, surpassing all other existing methods on the Middlebury online dataset leaderboard, shown in Tab.3. In addition, our EGLCR-Stereo model surpasses RAFT-Stereo and IGEV-Stereo by 16.5% on the bad2.0 (percentage of bad pixels whose error is greater than 2.0) metric. Furthermore, according to the disparity map generated from the online list in Fig. 7 , it is clear that our model demonstrates better performance on the large-disparity prediction and object-edge structural perception than the RAFT-Stereo.For ETH3D and KITTI, our EGLCR-Stereo surpasses recent methodologies such as IGEV-Stereo, CREStereo and CroCo, and sets a new standard in terms of the proportion of pixels with an error exceeding 0.5px (Bad0.5) and the average mean error (AvgErr), as detailed in Tab. 4. Meanwhile, the bad0.5 metric for non-occluded pixels has been enhanced by a minimum of 10% than other methods. Table .5 shows results of quantity comparison on KITTI dataset. Our EGLCR-Stereo achieves the state-of-the-art performance among all the published models, and especially obtains remarkable improvements in the metrics of the reflective region (i.e., 3/4/5-noc(ref)). other state-of-the-art (SOTA) methods in terms of the 1-pixel error rate metric. Besides, for the quarter-resolution images on the Middlebury dataset, our proposed model surpassed RAFT by about 4% in the 2-pixel error rate metric. Espeically, when we escalate the resolution of the predicted images (the disparity range of the target domain also increases), the advantages of our model become more apparent, outstripping RAFT directly by margins of 14.6% and 12.2% for half and full resolutions, respectively. In addition, our model exhibits a robust convergence performance in disparity prediction than the baseline model. As shown in Tab. 7, the disparity prediction result at only one iteration in the inference phase exceeds the baseline model by 62.3%. This superior performance is attributed to the scale attention module and wide receptive field of our model, which helps to avoid local optimal disparity points during the iterative optimization process. Furthermore, Fig. 9 displays the predicted disparities on some typical real-world scenes of StereoDriving. As shown in the rectangular box, our EGLCR-stereo obtains more satisfactory disparity details. 
conclusion: 
 In this article, we introduce a novel stereo-matching model named EGLCR-Stereo, designed for disparity estimation. The model employs an iterative disparity optimization strategy. In the EGLCR-Stereo framework, we develop a multi-scale similarity feature extraction module along with an edge structure-aware module. These modules are engineered to extract similarity features from stereo images across multiple scales and to refine edge structures. The multi-scale similarity features and the refined edge structures play a  
 
 Appendix: 2024-04-13 10:42. Page 3 of 1-9.2024-04-13 10:42. Page 4 of 1-9.2024-04-13 10:42. Page 6 of 1-9.2024-04-13 10:42. Page 7 of 1-9.2024-04-13 10:42. Page 8 of 1-9. 


Decision: The paper received mixed reviews. While it presents a promising approach to disparity estimation with clear strengths in robustness, generalization, and enhanced accuracy, it also has several limitations, including computational complexity, lack of novelty, and insufficient evaluation on additional benchmarks. Addressing these concerns, particularly through detailed discussions on computational costs, clearer differentiation from existing methods, and testing on the KITTI benchmark, will strengthen the paper.

Reviews: review: paper_summary: paper_summary: This paper proposes a method for disparity refinement to improve the performance of disparity maps by integrating edge, global, and local information. The method is validated on three datasets, ETH3D, Middlebury, and SceneFlow, and obtains pretty good results.
strength_weakness: strength_weakness: 1. The method improves Raft-Stereo by extracting and fusing the edge and scale-adaptive information to make the features more discriminative. Therefore the matching results are better.
2. The method is validated on multiple datasets to demonstrate its generalization ability.
limitations: limitations: 1. Didn't test the method on KITTI's online benchmark. It would be more convincing if this result were given.
2. The correlation between the edge guidance and the scale-adaptive attention is a bit poor, which makes this work feel a bit patchwork without a clear intention and backbone.
paper_summary: paper_summary: The proposed model EGLCR-Stereo utilizes edge structure information and multi-scale matching similarity features for better disparity estimation. It uses a lightweight network to predict the initial disparity, and then develops a multi-scale similarity feature extraction module to capture the fusion similarity information of stereo images across various scales. Moreover, it introduces an edge structureaware module to delineate edge information in complex scenes. Finally, it employs an iterative strategy for disparity estimation. Extensive experimental results demonstrate the effectiveness of the proposed model EGLCR-Stereo.
strength_weakness: strength_weakness: 1. This work creatively enhances the model's ability to perceive depth edges by constraining the edge information generated from depth and semantics.
2. Extensive experimental comparisons and ablations are conducted on various widely-used stereo matching datasets, which demonstrate that the proposed framework EGLCR-Stereo consistently outperforms state-of-the art methods.
limitations: limitations: 1. The proposed model EGLCR-Stereo should show the running distribution, e.g., model parameters, FLOPs and speed, etc.
2. In Table 1, the performance of RAFT lacks clear description.
3. Some bugs. In Formula 5, the definitions of some symbols are not given.
paper_summary: paper_summary: The paper introduces the EGLCR-Stereo model for stereo matching, which can enhance disparity estimation by leveraging edge structure information and adaptive scale attention mechanisms. This model is designed to address challenges such as disparity variations at object boundaries and performance under large disparity conditions. It consists of a multi-scale similarity feature extraction module, and an edge structure-aware module, and employs GRU-based iterative optimization for refining disparity estimations. The proposed approach is validated on multiple datasets, demonstrating superior performance in terms of accuracy and generalization compared to existing methods.
strength_weakness: strength_weakness: 1. The model is rigorously tested across multiple datasets, including both synthetic and real-world scenarios, demonstrating robustness and adaptability. The inclusion of ablation studies further strengthens the validity of the proposed enhancements.

2. EGLCR-Stereo achieves good results on several benchmarks, outperforming existing methods in terms of both accuracy and the ability to generalize across different domains. This is corroborated by quantitative results showing improvements over the baseline models.
limitations: limitations: 1. The utilization of edge information to optimize stereo matching is not a novel concept, as previously explored in works like EdgeStereo. In this paper, the integration of edge information into the Raft-Stereo framework, coupled with a basic attention feature fusion mechanism, appears to be a straightforward incremental work that may show few contributions to the stereo-matching community.

2. The model appears to be computationally intensive, potentially limiting its application in real-time or on-device scenarios. However, the paper lacks a detailed discussion of the actual computational costs, such as memory requirements and execution time. Given the fact that many "so-called " new methods based on the Raft-Stereo-like framework are known to be computationally demanding, the additional components introduced in this model, such as multi-scale features, might exacerbate this issue. It would be beneficial for the authors to provide empirical evidence demonstrating that the performance improvements are attributable to the novel aspects of the architecture rather than merely to an increase in the number of parameters.

3. Insufficient Discussion on Failure Cases: The paper could improve by providing more detailed discussions on any limitations or failure cases observed during the experiments. Understanding where the model falls short can be crucial for practical applications and future improvements.
paper_summary: paper_summary: This paper proposes an iterative-based stereo matching method named EGLCR-Stereo to improve the accuracy of disparity estimation. This model mainly contributes a multi-scale similarity feature extraction module and an edge structure-aware module to the baseline method (RAFT) in the disparity refinement process. Experiments on four popular stereo matching datasets, including Middlebury, ETH3D, and Scene Flow, show the effectiveness of EGLCR-Stereo.
strength_weakness: strength_weakness: + Technically, the usage of multi-scale feature and edge estimation is effective for the iterative-based disparity prediction, which is proved by the ablation study.

+ Evaluation on accuracy is adequate. As shown in the qualitative results, the EGLCR-Stereo successfully improves the disparity estimation accuracy in three kinds of scenes: the edges of fine structures, areas with large disparity, and cross-domain prediction.
limitations: limitations: - Lack of novelty. The idea of using multi-scale feature to enlarge the receptive field and capture global information in stereo matching has been proposed by "Edge supervision and multi-scale cost volume for stereo matching" (Image and Vision Computing, 2022, 117: 104336). While the difference between this paper and the proposed method is not clarified.

- Writing needs improvement. 
1) The contributions are not concisely summarized. In the contribution paragraphs, there are previously proposed techniques, e.g. group correlation [6] in line 219, which makes the novelty of this paper confusing.
2) There are many unexplained terms. E.g. in Table 1, the 'SSMS' and 'LSMS' are not explained. In Figure 2, the 'context network' is not explained or mentioned in other place of the paper. In Eq. (5), the $\mathcal{F}_{att}$ is not explained.
3) There are some inaccurate expressions leading to misunderstanding. E.g. in Figure 2, the $f_{con}^{(k)}$ may be miswritten and should be $f_{d}^{(k)}$.
4) References are not adequate. E.g. the baseline model (RAFT) is not explicitly cited in the main paper, though we can read it from Table 1. And the reference of the ETH3D dataset is lacked.

- Lack of evaluation on the inference speed of the proposed method.