A Smooth Transition Algorithm for Adjacent Panoramic Viewpoints Using Matched Delaunay Triangular Patches

Zhao, Pengcheng; Hu, Qingwu; Tang, Zilong; Ai, Mingyao

doi:10.3390/ijgi9100596

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…It is observed that image completion methods often lead to deformations in local rigid structures during the rectification process. Moreover, when addressing the boundaries of missing regions, noticeable boundary effects are common, as image completion methods need to ensure smooth transitions between the filled area and the surrounding region, leading to prominent boundary artifacts [55][56][57]. In addition, this article also presents other qualitative comparison results, as shown in Appendix A Figures A1 and A2.…”

Section: Qualitative Comparison Of Image Rectificationmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

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Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification.

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Section: Qualitative Comparison Of Image Rectificationmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Section: Qualitative Comparison Of Image Rectificationmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Preprint

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Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

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“…In recent years, the construction of projects such as “National 3D Real Scene” (Zhang, Huang, et al, 2021) and “Digital Twin” (Gu et al, 2020) has been gradually promoted throughout the country, and the representation of spatiotemporal objects in the real world has also been reformed and upgraded from a 2D plane to a 3D scene (Liu, 2019; Yu et al, 2021; Zhao et al, 2020). 3D real scene is a digital virtual space that represents and portrays the real world in a photorealistic, three‐dimensional, and sequential manner.…”

Section: Introductionmentioning

confidence: 99%