Online illumination estimation of outdoor scenes based on videos containing no shadow area
Real-time estimation of outdoor illumination is one of the key issues for ensuring the illumination consistency of augmented reality. In this paper, we propose a novel framework to estimate the dynamic illumi- nation of outdoor scenes based on an online video sequence captured by a fixed camera. All...
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| Published in | Science China. Information sciences Vol. 56; no. 3; pp. 1 - 11 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
SP Science China Press
01.03.2013
Springer Nature B.V |
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| Abstract | Real-time estimation of outdoor illumination is one of the key issues for ensuring the illumination consistency of augmented reality. In this paper, we propose a novel framework to estimate the dynamic illumi- nation of outdoor scenes based on an online video sequence captured by a fixed camera. All existing approaches are based on two assumptions, i.e. there exist some shadow areas in the scene and the distribution of the skylight is uniform over the sky. Both assumptions greatly simplify the problem of illumination estimation of outdoor scenes, but they also limit the applicability as well as the accuracy of these approaches. This paper presents a new approach that breaks these two hard constraints. It recovers the lighting parameters of outdoor scenes containing no shadow area through solving a constrained linear least squares problem. By representing the skylight as a parameterized model incorporating an occlusion coefficient, the proposed approach can handle the dynamic variation of non-uniform skylight distribution. Experimental results demonstrate the potential of our approach. |
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| AbstractList | Real-time estimation of outdoor illumination is one of the key issues for ensuring the illumination consistency of augmented reality. In this paper, we propose a novel framework to estimate the dynamic illumi- nation of outdoor scenes based on an online video sequence captured by a fixed camera. All existing approaches are based on two assumptions, i.e. there exist some shadow areas in the scene and the distribution of the skylight is uniform over the sky. Both assumptions greatly simplify the problem of illumination estimation of outdoor scenes, but they also limit the applicability as well as the accuracy of these approaches. This paper presents a new approach that breaks these two hard constraints. It recovers the lighting parameters of outdoor scenes containing no shadow area through solving a constrained linear least squares problem. By representing the skylight as a parameterized model incorporating an occlusion coefficient, the proposed approach can handle the dynamic variation of non-uniform skylight distribution. Experimental results demonstrate the potential of our approach. Real-time estimation of outdoor illumination is one of the key issues for ensuring the illumination consistency of augmented reality. In this paper, we propose a novel framework to estimate the dynamic illumination of outdoor scenes based on an online video sequence captured by a fixed camera. All existing approaches are based on two assumptions, i.e. there exist some shadow areas in the scene and the distribution of the skylight is uniform over the sky. Both assumptions greatly simplify the problem of illumination estimation of outdoor scenes, but they also limit the applicability as well as the accuracy of these approaches. This paper presents a new approach that breaks these two hard constraints. It recovers the lighting parameters of outdoor scenes containing no shadow area through solving a constrained linear least squares problem. By representing the skylight as a parameterized model incorporating an occlusion coefficient, the proposed approach can handle the dynamic variation of non-uniform skylight distribution. Experimental results demonstrate the potential of our approach. |
| Author | XING GuanYu ZHOU XueHong LIU YanLi QIN XueYing PENG QunSheng |
| AuthorAffiliation | State Key Laboratory of CAD＆CG, Zhejiang University, Hangzhou 310027, China School of Computer Science and Technology, Shandong University, Ji＇nan 250100, China; College of Computer Science, Sichuan University, Chengdu 610065, China |
| Author_xml | – sequence: 1 givenname: GuanYu surname: Xing fullname: Xing, GuanYu organization: State Key Laboratory of CAD&CG, Zhejiang University – sequence: 2 givenname: XueHong surname: Zhou fullname: Zhou, XueHong organization: State Key Laboratory of CAD&CG, Zhejiang University – sequence: 3 givenname: YanLi surname: Liu fullname: Liu, YanLi organization: College of Computer Science, Sichuan University – sequence: 4 givenname: XueYing surname: Qin fullname: Qin, XueYing organization: School of Computer Science and Technology, Shandong University – sequence: 5 givenname: QunSheng surname: Peng fullname: Peng, QunSheng email: peng@cad.zju.edu.cn organization: State Key Laboratory of CAD&CG, Zhejiang University |
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| CitedBy_id | crossref_primary_10_1016_j_cag_2013_10_030 crossref_primary_10_1016_j_compeleceng_2021_107057 crossref_primary_10_1109_TIP_2014_2369962 crossref_primary_10_1016_j_compeleceng_2021_106987 |
| Cites_doi | 10.1145/2070781.2024191 10.1002/cav.357 10.1109/ICCV.2009.5459163 10.1111/1467-8659.00513 10.5772/7125 10.1109/CAD/Graphics.2011.19 10.1145/1618452.1618476 10.1007/s00371-009-0342-4 10.1109/CVPR.2006.76 10.1109/34.946995 10.1145/383259.383310 10.1109/CVPR.2007.383258 10.1109/ICCV.2001.937585 10.1145/1186415.1186473 10.1145/2070781.2024180 10.1145/1409060.1409112 10.5220/0001099502550261 10.1145/1073204.1073232 10.1016/j.cag.2012.07.005 10.1109/CVPR.2011.5995738 10.1109/ICCV.2009.5459185 |
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| Keywords | outdoor scenes non-uniform skylight distribution online illumination estimation limited scene reconstruction videos containing no shadow areas |
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| Notes | 11-5847/TP outdoor scenes, online illumination estimation, videos containing no shadow areas, non-uniformskylight distribution, limited scene reconstruction Real-time estimation of outdoor illumination is one of the key issues for ensuring the illumination consistency of augmented reality. In this paper, we propose a novel framework to estimate the dynamic illumi- nation of outdoor scenes based on an online video sequence captured by a fixed camera. All existing approaches are based on two assumptions, i.e. there exist some shadow areas in the scene and the distribution of the skylight is uniform over the sky. Both assumptions greatly simplify the problem of illumination estimation of outdoor scenes, but they also limit the applicability as well as the accuracy of these approaches. This paper presents a new approach that breaks these two hard constraints. It recovers the lighting parameters of outdoor scenes containing no shadow area through solving a constrained linear least squares problem. By representing the skylight as a parameterized model incorporating an occlusion coefficient, the proposed approach can handle the dynamic variation of non-uniform skylight distribution. Experimental results demonstrate the potential of our approach. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 |
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| References_xml | – reference: Karsch K, Hedau V, Forsyth D, et al. Rendering Synthetic Objects into Legacy Photographs. In: Proc Siggraph Aisa, Hong Kong, 2011. 157:1–157:12 – reference: Chen X, Wang K, Jin X. Single image based illumination estimation for lighting virtual object in real scene. In: Proc. CAD/Graphics, Jinan, 2011. 450–455 – reference: Koppal S J, Narasimhan S G. Clustering appearance for scene analysis. In: Proc CVPR, New York, 2006. 1323–1330 – reference: Agrawal A, Raskar R, Chellappa R. Edge suppression by gradient field transformation using crossprojection tensors. In: Proc CVPR, New York, 2006. 301–308 – reference: Madsen C B, Nielsen M. Towards probe-less augmented reality-a position paper. In: Rroc GRAPP, Funchal, 2008. 255–261 – reference: HoiemD.EfrosA. A.HebertM.Automatic photo pop-upACM Trans Graph20052457758410.1145/1073204.1073232 – reference: BousseauA.ParisS.DurandF.User-assisted intrinsic imagesACM Trans Graph200928130:1130:1010.1145/1618452.1618476 – reference: RenP.WangJ.SnyderJ.Pocket ReflectometryACM Trans Graph2011304510.1145/2010324.1964940 – reference: Debevec P. Rendering synthetic objects into real scenes: Bridging traditional and image-based graphics with global illumination and high dynamic range photography. In: Proc SIGGRAPH, Orlando, 1998. 189–198 – reference: Shen L, Yeo Y. Intrinsic images decomposition using a local and global sparse representation of reflectance. In: Proc CVPR, Colorado, 2011. 697–704 – reference: Saxena A, Sun M, NG A Y. Make3d: depth perception from a single still image. In: Proc AAAI, Chicago, 2008. 1571–1576 – reference: Jacobs N, Roman N, Pless R. Consistent temporal variations in many outdoor scenes. In: Proc CVPR, Minneapolis, 2007. 1–6 – reference: LiuY.QinX.XingG.A new approach to illumination estimation based on statistical analysis for augmented realityComput Anim Virt Worlds201021321330 – reference: ZhangY.YangY.Multiple illuminant direction detection with application to image synthesisIEEE Trans Pattern Anal Mach Intell20012391592010.1109/34.946995 – reference: GibsonS.HowardT.HubboldR.Flexible image-based photometric reconstruction using virtual light sourcesComput Graph Forum20012020321410.1111/1467-8659.00513 – reference: LiuY.QinX.XuS.Light source estimation of outdoor scenes for mixed realityVis Comput20092563764610.1007/s00371-009-0342-4 – reference: Horry Y, Anjyo K I, Arai K. Tour into the picture: using a spidery mesh interface to make animation from a single image. In: Proc SIGGRAPH, Los Angeles, 1997. 225–232 – reference: Xue M, Ling H, Jacobs D. Sparse representation of cast shadows via l1-regularized least squares. In: Proc ICCV, Kyoto, 2009. 583–590 – reference: SinhaS. N.SteedlyD.SzeliskiR.Interactive 3d architectural modeling from unordered photo collectionsACM Trans Graph200827159:1159:1010.1145/1409060.1409112 – reference: Nakamae E, Harada K, Ishizaki T, et al. A montage method: the overlaying of the computer generated images onto a background photograph. In: Proc SIGGRAPH, Dallas, 1986. 207–214 – reference: BarrowH. G.TenenbaumJ. M.Recovering intrinsic scene characteristics from imagesComput Vis Syst19783326 – reference: Madsen C B, Brajesh B L. Outdoor illumination estimation in image sequences for augmented reality. In: Proc GRAPP, Vilamoura, 2011. 129–139 – reference: Lalonde J F, Efros A A, Narasimhan S G. Estimating Natural Illumination from a Single Outdoor Image. In: Proc. ICCV, Kyoto, 2009. 183–190 – reference: Oh B M, Chen M, Dorsey J, et al. Image-based modeling and photo editing. In: Proc SIGGRAPH, Los Angeles, 2001. 433–442 – reference: DongY.TongX.PellaciniF.AppGen: interactive material modeling from a single imageACM Trans Graph20113014610.1145/2070781.2024180 – reference: Stumpfel J, Jones A, Wenger A, et al. Direct HDR capture of the sun and sky. In: Proc AfriGraph, Stellenbosch, 2004. 145–149 – reference: Bell M, Freeman W T. Learning local evidence for shading and reflectance. In: Proc ICCV, Vancouver, 2001. 670–677 – reference: XingG.LiuY.QinX.A practical approach for real time illumination estimation of outdoor videosComput Graph20123685786510.1016/j.cag.2012.07.005 – ident: 4780_CR9 doi: 10.1145/2070781.2024191 – volume: 21 start-page: 321 year: 2010 ident: 4780_CR11 publication-title: Comput Anim Virt Worlds doi: 10.1002/cav.357 – ident: 4780_CR8 doi: 10.1109/ICCV.2009.5459163 – ident: 4780_CR27 – volume: 3 start-page: 3 year: 1978 ident: 4780_CR13 publication-title: Comput Vis Syst – volume: 20 start-page: 203 year: 2001 ident: 4780_CR4 publication-title: Comput Graph Forum doi: 10.1111/1467-8659.00513 – ident: 4780_CR25 doi: 10.5772/7125 – ident: 4780_CR7 doi: 10.1109/CAD/Graphics.2011.19 – volume: 28 start-page: 130:1 year: 2009 ident: 4780_CR14 publication-title: ACM Trans Graph doi: 10.1145/1618452.1618476 – volume: 25 start-page: 637 year: 2009 ident: 4780_CR10 publication-title: Vis Comput doi: 10.1007/s00371-009-0342-4 – ident: 4780_CR26 doi: 10.1109/CVPR.2006.76 – volume: 23 start-page: 915 year: 2001 ident: 4780_CR5 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.946995 – ident: 4780_CR24 – ident: 4780_CR1 – ident: 4780_CR22 – ident: 4780_CR21 doi: 10.1145/383259.383310 – volume: 30 start-page: 45 year: 2011 ident: 4780_CR19 publication-title: ACM Trans Graph – ident: 4780_CR28 doi: 10.1109/CVPR.2007.383258 – ident: 4780_CR16 – ident: 4780_CR17 doi: 10.1109/ICCV.2001.937585 – ident: 4780_CR2 doi: 10.1145/1186415.1186473 – volume: 30 start-page: 146 year: 2011 ident: 4780_CR18 publication-title: ACM Trans Graph doi: 10.1145/2070781.2024180 – volume: 27 start-page: 159:1 year: 2008 ident: 4780_CR20 publication-title: ACM Trans Graph doi: 10.1145/1409060.1409112 – ident: 4780_CR6 doi: 10.5220/0001099502550261 – volume: 24 start-page: 577 year: 2005 ident: 4780_CR23 publication-title: ACM Trans Graph doi: 10.1145/1073204.1073232 – volume: 36 start-page: 857 year: 2012 ident: 4780_CR12 publication-title: Comput Graph doi: 10.1016/j.cag.2012.07.005 – ident: 4780_CR15 doi: 10.1109/CVPR.2011.5995738 – ident: 4780_CR3 doi: 10.1109/ICCV.2009.5459185 |
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| SubjectTerms | Augmented reality Coefficient of variation Computer Science Constraints Dynamics Illumination Information Systems and Communication Service Least squares method Lighting Occlusion On-line systems Online Outdoor Research Paper Shadows Skylights 估计 光照 在线 基础 室外场景 户外照明 视频序列 阴影区 |
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| Title | Online illumination estimation of outdoor scenes based on videos containing no shadow area |
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