Title: Depth-Supervised Fusion Network for Seamless-Free Image Stitching
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image Fusion
Abstract: Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, we propose a depth-consistency-constrained seamless-free image stitching method. First, to tackle the multi-view alignment difficulties caused by parallax, a multi-stage mechanism combined with global depth regularization constraints is developed to enhance the alignment accuracy of the same apparent target across different depth ranges. Second, during the multi-view image fusion process, an optimal stitching seam is determined through graph-based low-cost computation, and a soft-seam region is diffused to precisely locate transition areas, thereby effectively mitigating alignment errors induced by parallax and achieving natural and seamless stitching results. Furthermore, considering the computational overhead in the shift regression process, a reparameterization strategy is incorporated to optimize the structural design, significantly improving algorithm efficiency while maintaining optimal performance. Extensive experiments demonstrate the superior performance of the proposed method against the existing methods. Code is available at https://github.com/DLUT-YRH/DSFN.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel two-stage image stitching method enhanced with deep supervision, targeting the challenge of stitching images with significant parallax. By combining linear and nonlinear deformations alongside soft seam region fusion, the method achieves high-quality, seamless image stitching. Reparameterization further boosts computational efficiency. Experimental results validate the effectiveness of this method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper presents a two-stage image stitching method designed to achieve high-quality stitching of large parallax images. This method introduces deep supervision to address the challenges associated with aligning images that have significant differences in perspective and depth.\n2.  Reparameterization is employed to enhance the computational performance of the stitching process.  This technique not only improves the efficiency of the method but also reduces the consumption of computing resources, making it more practical for real-world applications.\n3. This paper also incorporates depth information into the image stitching.  By leveraging depth cues, the method optimizes the stitching of large parallax images, leading to more accurate and visually pleasing results. \n\nWeakness:\nThe manuscript presents a multifaceted evaluation of the proposed method. However, the visual comparisons are limited, and the robustness under different imaging conditions requires further investigation. Additional experimental results are recommended.\n\nQUESTIONS:\n1. Does the method proposed in this paper have a strong dependence on the generation quality of depth maps? Will the absence of an effective depth map lead to errors in the stitched image?\n2. Why is the method of combining linear transformation and nonlinear transformation adopted for image alignment? Have you ever attempted to directly achieve alignment through nonlinear deformation?\n3. In the ablation experiment, why do some indicators show some improvement when the mesh constraint is removed? Does it mean that the mesh constraint is meaningless?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work aims for image stitching at challenging scenes with low textures and large parallax. The overall pipeline is consists of two stage; transformation estimation and multi-view fusion stage. At the transformation stage, image features from ResNet50 are aggregated with attention modules, and the regression network estimates the DLT parameters for coarse warping and a residual deformation field.\nIn this stage, the authors newly suggested the mesh loss (edge + angle) which preserves the shape consistency of the mesh, and the depth loss which reduces the difference between relative depths earned from DepthAnything in the overlapping region.\nAt the fusion stage, the authors proposed a soft-seam estimation module, which outputs soft-seam region from the mask of warped target and reference images. Depth values are again regularized to have similar values in warped pixels at this stage.\nLastly, authors suggested to use RepBlock (RepVGG) at the regression block of the transformation estimation stage. Instead of naively incorporating the RepVGG layer, they proposed a reparameterization algorithm that selectively integrates the RepBlock or a standard convolution layer.\nThe quality of the proposed method was evaluated on the UDIS-D dataset through quantitative, qualitative, and user studies.                    . \t`\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n1. The authors adopted a monocular depth estimation network to resolve the large parallax caused by the big depth difference. The authors suggested a regularization loss that can incorporate prior depth knowledge into the end-to-end image stitching pipeline.\n2. The authors suggested a novel approach to consider seam as a candidate area and adopted a soft seam estimation module to estimate the blending regions. The qualitative result of ablation shows that such soft blending achieves a sharp yet smooth transition in the image gradient space.\n\nWeakness.\n1. In the transformation estimation stage, it’s hard to differentiate the contribution of this work and the contribution of previous studies (FCA block and alignment loss). Dividing the sections into preliminaries and the contribution would be helpful.\n2. There is no explanation of the operations or the architecture of the Soft-Seam-Estimation module which is the key contribution. The paper only demonstrates the input and output. Moreover,  the loss equations (10), (11), and (12) are not intuitive enough to understand their objective in the optimization. What kind of pixel calculation does ⊙? Why does this operation work on the inverted and non-inverted region mask?\n3. In the ablation study (Figure 5) the result of the full model does not excel in most evaluation metrics except SIQE. This violates the argument that adopting the regularization and depth prior will enhance the stitching quality. Is there a reason for the mismatch between qualitative results and quantitative results?\n\nQUESTIONS:\n1. The alignment of two depth maps without camera positions of two views can not be trivially done with 2D warping. The minimum and maximum values taken for depth normalization should be carefully chosen depending on the distance between the surface and the camera, to make warped affine depth maps to be numerically similar. However, the paper does not include the depth normalization method in both the transformation estimation module and the fusion module. In ablation, there was no significant quality enhancement by the depth loss and the regression loss. I doubt the correctness and effectiveness of depth supervision modules.\n\n2. Most of the network architecture specifications are excluded in this paper. 1) There is no exact explanation of the soft-seam estimation module’s operation. 2) It’s hard to find where the RBA algorithm was adopted since the regression module's architecture was not described.\n\n3. The quantitative improvements by the regularizations are numerically weak (Table 2) compared to the noticeable quality enhancement in Figure 6. Please analyze this mismatch and supplement the effectiveness of the suggested contributions.\n\n4. What is the inherent limitation of RepBlocks? (line 193). Please explain the specific behavior. Why does it need to be selectively incorporated instead of completely removing it?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a depth-supervised image stitching method, aiming to solve the stitching challenge of images with depth differences. This method achieves precise alignment of targets at different depths through two-stage deformation estimation combined with depth consistency constraints. Meanwhile, the strategy is determined through the soft seam area, and smooth fusion is achieved by combining the deep continuity constraint to alleviate the problems of ginning and misalignment. The re-parameterization strategy of shift regression further improves adaptability and reduces computational overhead.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The introduction of deep supervision effectively addresses the influence of depth disparities on image alignment, showcasing a robust solution to this critical challenge.\n2. The soft seam fusion strategy significantly mitigates ghosting artifacts, resulting in superior visual quality and a more aesthetically pleasing output.\n3. The reparameterization approach minimizes computational overhead while enhancing the overall performance of the method, demonstrating its efficiency.\n4. The incorporation of depth maps to enhance the quality of image stitching represents a promising avenue for future research, highlighting its potential to advance the field.\n\nWeaknesses:\n1. It seems that the method proposed in this paper may have relatively high requirements regarding the generation quality of depth maps, which could potentially affect its applicability in certain scenarios. Could you provide more specific details on this aspect?\n2. Given the complexity of the depth estimation method, there may be notable challenges in optimizing it to meet the demands of real-time applications.\n\nQUESTIONS:\n1. In cases where the input image exhibits no significant depth variation, is it still possible for this method to maintain accuracy? \n2. In figure2, why is the color difference at the junction of the second group of stitched images larger compared to some comparison methods? What could be the possible reasons for this phenomenon? Has this type of image been evaluated in a user study, and what were the rating results?\n3. Will the method introduced in this paper be made open-source? No relevant links or descriptions have been identified thus far.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a depth-supervised, seam-free image stitching framework tailored for large-parallax scenarios. It introduces a depth-aware two-stage transformation estimation, a soft-seam multi-view fusion module for seamless transitions, and a reparameterization-based shift regression strategy to improve computational efficiency. Experiments show that this method has certain effectiveness.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The proposed depth-aware two-stage transformation estimation module effectively mitigates alignment issues caused by depth variations. The motivation is clear, and the design is well-justified.\n2. The soft-seam multi-view fusion module relaxes the definition of stitching seams, significantly improving the naturalness of image transitions.\n3. The ablation studies are thorough and systematic, clearly demonstrating the contributions of each module and loss component.\n\nWeaknesses\n1. The $L_{depth}$ relies entirely on the depth maps provided by Depth Anything. However, as a general-purpose model not specifically designed for image stitching tasks, Depth Anything may introduce biases from its training data. Consequently, the loss may misfire in regions with noisy or discontinuous depth maps. It can penalize correct predictions, introduce misleading supervision signals, and steer the main model in the wrong direction.\n2. All experiments are conducted on standard-sized images, whereas real-world stitching tasks often involve ultra-high-resolution images or multi-scale inputs. The paper lacks experiments under such practical and challenging scenarios.\n3. The paper does not include comparisons with image stitching methods based on generative models[1], which are becoming increasingly relevant in recent research.\n4. The paper uses a large number of symbols in its equations, but not all of them are clearly defined. For example, $p_k$ in Equ. (2). It is recommended that the authors include a table of symbol definitions to improve readability.\n5. There are some grammatical errors in the paper, such as \"a depth-supervised image stitching\" in line 43 and \"state-of-the-arts\" in line 64. It is recommended that the authors carefully review and correct the grammatical issues throughout the paper.\n\n[1] Recdiffusion: Rectangling for image stitching with diffusion models. CVPR 2024.\n\nQUESTIONS:\nPlease refer to the weaknesses proposed in the strength and weakness section.\nAnd I have some minor questions:\n1. What are the time and space complexities of the proposed method?\n2. All experiments in the paper are conducted on stitching two images. However, real-world applications such as 360° panoramic stitching often involve stitching 5 to 10 images and require addressing challenges like loop consistency and global error propagation. I’m curious about how the proposed method performs in such scenarios.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-06"}