Stereoscopic Image Stitching Based on a Hybrid Warping Model

Yan, Weiqing; Hou, Chunping; Lei, Jianjun; Fang, Yuming; Gu, Zhouye; Ling, Nam

doi:10.1109/tcsvt.2016.2564838

Cited by 31 publications

(29 citation statements)

References 47 publications

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“…Wang et al [24] combined a constrained projective and a shape-preserving warp to make the stitching as natural as possible. Although better than previous methods [26,30], their method does not consider the boundary regularity constraint and thus may lose much information after cropping. In this paper, we aim to construct a global optimization to simultaneously stitch left and right views, while ensuring the disparity consistency, least local distortions and boundary regularity.…”

Section: Stereoscopic Image and Video Editingmentioning

confidence: 96%

“…However, the stitching of two views is not treated as a global optimization, which cannot ensure the optimal alignment and warping and thus may introduce noticeable distortions. Yan et al [26] proposed stereoscopic stitching based on a combination of projective and content-preserving warping. Compared with [30], their stitching is more robust and effective.…”

Section: Stereoscopic Image and Video Editingmentioning

confidence: 99%

“…Although successfully keeping the disparity consistency, they did not combine the stitching process in left and right views in a unified optimization framework and therefore cannot ensure an optimal solution with minimum distortions. Recently, Yan et al [26] and Wang et al [24] proposed hybrid warping and natural shape-preserving methods to further improve the stitching performance. However, their methods cannot ensure the global optimality and boundary regularity.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it would be preferable if the final panorama could have a more regular shape. However, previous methods like [26] usually generate panoramas with irregular boundaries, and the final panoramas after cropping may lose much information, which degrades the wide-angle viewing effects.…”

Section: Introductionmentioning

confidence: 99%

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Stereoscopic image stitching with rectangular boundaries

2019

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This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience.

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Section: Stereoscopic Image and Video Editingmentioning

confidence: 96%

Section: Stereoscopic Image and Video Editingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stereoscopic image stitching with rectangular boundaries

2019

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“…With the development of information and communications technology (ICT), there is a growing demand in various fields, such as virtual reality (VR), image security, entertainment, aerial image, and mobile applications for panoramic images and videos with a wide angle of view [1][2][3][4]. Algorithms for aligning and stitching images into a seamless mosaic image are widely used in computer vision [5][6][7][8]. The estimation of the correct alignment relating various pairs of images, the choice of a final compositing surface for warping aligned images, and the seamless cutting and blending of overlapping images are required for image stitching even in the presence of parallax, lens distortion, scene motion, and exposure differences.…”

Section: Introductionmentioning

confidence: 99%

Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments

Park

Shim

Lee

et al. 2019

Sensors

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In this paper, a multi-frame based homography estimation method is proposed for video stitching in static camera environments. A homography that is robust against spatio-temporally induced noise can be estimated by intervals, using feature points extracted during a predetermined time interval. The feature point with the largest blob response in each quantized location bin, a representative feature point, is used for matching a pair of video sequences. After matching representative feature points from each camera, the homography for the interval is estimated by random sample consensus (RANSAC) on the matched representative feature points, with their chances of being sampled proportional to their numbers of occurrences in the interval. The performance of the proposed method is compared with that of the per-frame method by investigating alignment distortion and stitching scores for daytime and noisy video sequence pairs. It is shown that alignment distortion in overlapping regions is reduced and the stitching score is improved by the proposed method. The proposed method can be used for panoramic video stitching with static video cameras and for panoramic image stitching with less alignment distortion.

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GreenWarps: A Two-Stage Warping Model for Stitching Images Using Diffeomorphic Meshes and Green Coordinates

Jacob

Das

2019

Lecture Notes in Computer Science

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Stereoscopic Image Stitching Based on a Hybrid Warping Model

Cited by 31 publications

References 47 publications

Stereoscopic image stitching with rectangular boundaries

Stereoscopic image stitching with rectangular boundaries

Multi-Frame Based Homography Estimation for Video Stitching in Static Camera Environments

GreenWarps: A Two-Stage Warping Model for Stitching Images Using Diffeomorphic Meshes and Green Coordinates

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