Abstract
Image and video stitching have made tremendous progress in the construction of wide field-of-view (FOV). However, some long-term challenges still exist, including wide baselines between cameras, large parallaxes, and low texture in overlapping areas. The augmented virtual environment (AVE) captures videos as live textures of 3D models in a virtual environment, and provides another 3D solution to overcome the aforementioned challenges. Existing AVE methods primarily follow from video projection, and cannot produce satisfactory stitching results compared with image stitching. In this paper, we propose a novel model-guided 3D stitching algorithm for AVE. The algorithm recovers an approximate 3D model for each video streaming and optimizes the warping of the models to meet the requirements of feature point matching of the 3D models from adjacent videos. Compared with previous state-of-the-art methods, experiment results illustrate that our method significantly improves the stitching quality.
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References
Anderson R, Gallup D, Barron J T, et al. Jump: virtual reality video. ACM Trans Graph, 2016, 35: 1–13
Matzen K, Cohen M F, Evans B, et al. Low-cost 360 stereo photography and video capture. ACM Trans Graph, 2017, 36: 148
Zhu Z, Lu J, Wang M, et al. A comparative study of algorithms for realtime panoramic video blending. IEEE Trans Image Process, 2018, 27: 2952–2965
Zaragoza J, Chin T J, Brown M S, et al. As-projective-as-possible image stitching with moving DLT. In: Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013. 2339–2346
Chang C H, Sato Y, Chuang Y Y. Shape-preserving half-projective warps for image stitching. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. 3254–3261
Lin C C, Pankanti S U, Ramamurthy K N, et al. Adaptive as-natural-as-possible image stitching. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015. 1155–1163
Zhang F, Liu F. Parallax-tolerant image stitching. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014. 3262–3269
Chen Y S, Chuang Y Y. Natural image stitching with the global similarity prior. In: Proceedings of European Conference on Computer Vision, 2016. 186–201
Li J, Wang Z, Lai S, et al. Parallax-tolerant image stitching based on robust elastic warping. IEEE Trans Multimedia, 2018, 20: 1672–1687
Zhang G, He Y, Chen W, et al. Multi-viewpoint panorama construction with wide-baseline images. IEEE Trans Image Process, 2016, 25: 3099–3111
Lin K, Jiang N, Cheong L F, et al. SEAGULL: seam-guided local alignment for parallax-tolerant image stitching. In: Proceedings of European Conference on Computer Vision, 2016. 370–385
Sawhney H, Arpa A, Kumar R, et al. Video flashlights: real time rendering of multiple videos for immersive model visualization. In: Proceedings of Eurographics Workshop on Rendering, 2002. 157–168
Neumann U, You S, Hu J, et al. Augmented virtual environments (AVE): dynamic fusion of imagery and 3D models. In: Proceedings of IEEE Virtual Reality, 2003. 61–67
Sebe I O, Hu J, You S, et al. 3D video surveillance with augmented virtual environments. In: Proceedings of ACM SIGMM Workshop on Video Surveillance, 2003. 107–112
DeCamp P, Shaw G, Kubat R, et al. An immersive system for browsing and visualizing surveillance video. In: Proceedings of ACM International Conference on Multimedia, 2010. 371–380
Zhong Z, Jingdi Y, Jin Y, et al. Method for 3D scene structure modeling and camera registration from single image. US 20160249041 A1, 2016
Szeliski R. Image alignment and stitching: a tutorial. FNT Comput Graph Vision, 2007, 2: 1–104
Lyu W, Zhou Z, Chen L, et al. A survey on image and video stitching. Virtual Reality Intell Hardware, 2019, 1: 55–83
Zheng J, Wang Y, Wang H, et al. A novel projective-consistent plane based image stitching method. IEEE Trans Multimedia, 2019, 21: 2561–2575
Zhang Y, Lai Y K, Zhang F L. Content-preserving image stitching with piecewise rectangular boundary constraints. IEEE Trans Visual Comput Graph, 2021, 27: 3198–3212
Lee K-Y, Sim J-Y. Warping residual based image stitching for large parallax. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 8198–8206
Li J, Deng B, Tang R, et al. Local-adaptive image alignment based on triangular facet approximation. IEEE Trans Image Process, 2020, 29: 2356–2369
Li A, Guo J, Guo Y. Image stitching based on semantic planar region consensus. IEEE Trans Image Process, 2021, 30: 5545–5558
Jiang W, Gu J. Video stitching with spatial-temporal content-preserving warping. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015. 42–48
Perazzi F, Sorkine-Hornung A, Zimmer H, et al. Panoramic video from unstructured camera arrays. Comput Graph Forum, 2015, 34: 57–68
Huang H, Liu H, Zhang L. VideoWeb: space-time aware presentation of a videoclip collection. IEEE J Emerg Sel Top Circ Syst, 2014, 4: 142–152
Tompkin J, Kim K I, Kautz J, et al. Videoscapes: exploring sparse, unstructured video collections. ACM Trans Graph, 2012, 31: 1–12
Li C, Liu Z, Zhao Z, et al. A fast fusion method for multi-videos with three-dimensional GIS scenes. Multimed Tools Appl, 2021, 80: 1671–1686
Meng M, Zhou Y, Tan C, et al. Viewpoint quality evaluation for augmented virtual environment. In: Pacific-Rim Conference on Multimedia. Cham: Springer, 2018. 223–234
Zhou Y, Cao M, You J, et al. MR video fusion: interactive 3D modeling and stitching on wide-baseline videos. In: Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology, 2018. 17
Barnich O, van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process, 2011, 20: 1709–1724
Hoiem D, Efros A A, Hebert M. Automatic photo pop-up. ACM Trans Graph, 2005, 24: 577–584
Hoiem D, Efros A A, Hebert M. Recovering surface layout from an image. Int J Comput Vis, 2007, 75: 151–172
Guillou E, Meneveaux D, Maisel E, et al. Using vanishing points for camera calibration and coarse 3D reconstruction from a single image. Visual Comput, 2000, 16: 396–410
Lowe D G. Distinctive image features from scale-invariant keypoints. Int J Comput Vision, 2004, 60: 91–110
Fischler M A, Bolles R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM, 1981, 24: 381–395
von Gioi R G, Jakubowicz J, Morel J M, et al. LSD: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell, 2010, 32: 722–732
Zhu Z, Huang H Z, Tan Z P, et al. Faithful completion of images of scenic landmarks using internet images. IEEE Trans Visual Comput Graph, 2016, 22: 1945–1958
Kwatra V, Schödl A, Essa I, et al. Graphcut textures: image and video synthesis using graph cuts. ACM Trans Graph, 2003, 22: 277–286
Segal M, Korobkin C, Widenfelt R V. Fast shadows and lighting effects using texture mapping. In: Proceedings of ACM International Conference on Computer Graphics and Interactive Techniques, 1992. 249–252
Debevec P, Yu Y, Borshukov G. Efficient view-dependent image-based rendering with projective texture-mapping. In: Proceedings of the 9th Eurographics Rendering Workshop, 1998. 105–116
Acknowledgements
This work was supported by National Key Research and Development Program of China (Grant Nos. 2018YFB2100601, 2018YFB2100602) and National Natural Science Foundation of China (Grant No. 61872023).
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Appendixes A-E. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
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Zhou, Z., Meng, M., Zhou, Y. et al. Model-guided 3D stitching for augmented virtual environment. Sci. China Inf. Sci. 66, 112106 (2023). https://doi.org/10.1007/s11432-021-3323-2
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DOI: https://doi.org/10.1007/s11432-021-3323-2