Title: Image Stitching in Adverse Condition: A Bidirectional-Consistency Learning Framework and Benchmark
Venue: NeurIPS 2025
Accepted: yes
Keywords: image stitching

deep learning

computervision
Abstract: Deep learning-based image stitching methods have achieved promising performance on conventional stitching datasets. However, real-world scenarios may introduce challenges such as complex weather conditions, illumination variations, and dynamic scene motion, which severely degrade image quality and lead to significant misalignment in stitching results. To solve this problem, we propose an adverse condition-tolerant image stitching network, dubbed ACDIS. We first introduce a bidirectional consistency learning framework, which ensures reliable alignment through an iterative optimization paradigm that integrates differentiable image restoration and Gaussian-distribute encoded homography estimation. Subsequently, we incorporate motion constraints into the seamless composition network to produce robust stitching results without interference from moving scenes. We further propose the first adverse scene image stitching dataset, which covers diverse parallax and scenes under low-light, haze, and underwater environments. Extensive experiments show that the proposed method can generate visually pleasing stitched images under adverse conditions, outperforming state-of-the-art methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose an image stitching method designed for adverse conditions by leveraging a Gaussian distribution to encode the displacement space.\nTo address image degradation caused by severe weather, they introduce a differentiable image restoration module, whose output is subsequently integrated into MSC.\nAlthough image stitching has been studied under various conditions, the authors construct a dedicated dataset for extreme scenarios with insufficient visual cues, providing adverse weather image pairs along with corresponding ground truth for reliable evaluation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper presents a reliable method for image stitching in degraded conditions. The authors effectively integrate the image restoration and warping modules in an efficient and cohesive manner.\n2. A new dataset including images from adverse weather situations and their ground truth.\n\nWeaknesses\n1. Insufficient comparison with existing panoramic datasets, such as the UDIS dataset.\n2. Lack of analysis of the proposed model’s performance under normal (non-adverse) conditions.\n\nQUESTIONS:\n1. The authors should include a more thorough comparison with existing panoramic datasets, such as the UDIS dataset.\n\n2. The authors should analyze the proposed model’s performance under normal (non-adverse) conditions.\n\nLIMITATIONS:\nThere is a lack of discussion on limitations or future work. To ensure the proposed model is applicable in a wide range of scenarios, it should be evaluated not only under extreme conditions such as adverse weather but also under normal conditions. However, such analysis is missing, making it difficult to identify the model’s limitations in general settings. The authors should address this gap by including a discussion of relevant limitations, which could help pave the way for future improvements.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe article proposes a robust deep image stitching network called ACDIS, which addresses the impact of adverse conditions such as complex weather, lighting changes, and dynamic scene motions in real - world scenarios on image stitching. It introduces a bidirectional consistency learning framework that combines differentiable image restoration with Gaussian - distributed homography estimation, achieving reliable alignment.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. The authors constructed the first image stitching dataset named ASIS, which includes various adverse scenarios such as low - light conditions, foggy weather, and underwater environments. They conducted extensive experiments to verify the effectiveness of the proposed method.\n2. The proposed ASIS dataset provides a valuable benchmark for the evaluation of image stitching tasks under adverse conditions\n\nWeaknesses：\n1. Although the article introduces motion constraints in the seamless stitching network, the method may only be able to handle dynamic scene changes within a certain range. For more complex dynamic scenes, such as those with multiple moving objects or objects moving at high speeds, the article does not elaborate on how to more effectively address the image stitching problem under these complex dynamic scenarios. Further exploration and improvement of the motion constraint mechanism could be made to adapt to a wider range of dynamic scenes.\n2. The lack of interpretability is a notable drawback. Although the paper provides detailed technical descriptions, it falls short in offering in-depth explanations and analyses of the principles and processes of the core methods, such as the bidirectional-consistency learning framework and the recursive parameterized homography estimation module. For instance, the paper does not provide sufficient intuitive explanations regarding why the bidirectional optimization approach can enhance alignment reliability more effectively, as well as the underlying mechanisms of how the recursive parameterized homography estimation module gradually approximates the true homography transformation during the iterative process.\n3. The proposed ACDIS network, which includes multiple modules and iterative optimization processes, may suffer from relatively low computational efficiency. This could be a significant limitation in practical applications, especially in scenarios where real-time performance is required. The authors should consider optimizing the network structure or algorithmic flow to improve its speed and efficiency.\n\nQUESTIONS:\nPlease see the weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method that effectively handles various adverse factors in image stitching. It presents a novel approach for computing the correlation volume and successfully integrates an image enhancement module with the homography estimation module. As a result, the method demonstrates superior stitching performance. Additionally, the paper introduces a new dataset that reflects these diverse and challenging conditions.\n\nSTRENGTHS AND WEAKNESSES:\n**S1.** The effective integration of image enhancement into the homography estimation process enables more accurate results compared to using enhancement merely as a preprocessing step.\n\n**S2.** It proposes a method for utilizing a correlation volume to handle image pairs with large baselines.\n\n**S3.** Factors affecting image quality are incorporated into the DIR module by leveraging inductive biases.\n\n**S4.** The paper contributes a novel benchmark dataset.\n\n**W1.** The DIR module may struggle to address types of image degradation that are not explicitly covered by its pre-defined blocks.\n\n**W2.** The inclusion of a sampling process reduces the method's efficiency.\n\n**W3.** The DIR, BCDA, and MSC modules are trained separately, rather than through a single, end-to-end process.\n\n**W4.** Comparative experiments against other methods on the newly proposed dataset are not included in the paper.\n\nQUESTIONS:\n**Q1.** I have a question regarding the process of generating homographies for training. To learn the Gaussian distribution, it seems both the mean (μ) and standard deviation (σ) must be specified. Could you clarify how these are determined? Specifically, is the mean (μ) sampled from a uniform distribution, and is the final displacement value then sampled from the resulting normal distribution, N(μ, σ)?\n\n**Q2.** Regarding the DIR module, would using a generic CNN be less effective than the current design with its specialized, hand-crafted blocks? Was this alternative considered?\n\n**Q3.** Could you clarify why the mACE metric is not reported in Table 1?\n\n**Q4.** How does the total training time of the proposed method compare to that of other existing methods?\n\nLIMITATIONS:\n**L1.** The method requires input images to be of a fixed size.\n\n**L2.** Other limitations are as detailed in the main 'Weaknesses' section.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
A method for image stitching is proposed which is designed to handle adverse lightening and haze conditions. The proposed method is in two steps : first, two images alignment using consistency between the two images, and second, two images composition able to handle moving objects. A new dataset with adverse lightening and haze conditions for image stitching is introduced with 2,250 pairs of images. Comparative experiments are proposed  with 5 other methods on the proposed dataset. An ablation study for each step is also proposed.

STRENGTHS AND WEAKNESSES:
If the proposed structure seems standard, several contributions are proposed in the steps. The methods is not always clearly described and explained. For instance, in section 3.2, the equation (5) is not related to Beer-Lamber law. It is difficult to understand the processing described in section 3.1 and 3.2. For instance, in section 3.2, how is removed the mu_GT which is not known ?  The obtained results are visually not always convincing, like in Fig 14 and Fig 15, compared to other methods. It is only the two images stitching which is discussed and not the stitching of series of images.

QUESTIONS:
May you better explain the way the "Recursive Parameterized Homography Estimation" module is working ?
May you better explain the way the "Bidirectional-Consistency Learning Framework" is working ?
How can be performed the stitching of series of images ? 
What about the property of the youtube video used for building the proposed dataset ?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03