Inverse Compositional Estimation of 3D Pose And Lighting in Dynamic Scenes
In this paper, we show how we can estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D rarr 3D rarr 2D transformation. This...
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| Vydáno v: | IEEE transactions on pattern analysis and machine intelligence Ročník 30; číslo 7; s. 1300 - 1307 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Los Alamitos, CA
IEEE
01.07.2008
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 0162-8828, 1939-3539 |
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| Abstract | In this paper, we show how we can estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D rarr 3D rarr 2D transformation. This also allows us to extend traditional two-frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speedup over existing methods while retaining a high level of accuracy. |
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| AbstractList | In this paper, we show how to estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D --> 3D --> 2D transformation. This also allows us to extend traditional two frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speed-up over existing methods while retaining a high level of accuracy.In this paper, we show how to estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D --> 3D --> 2D transformation. This also allows us to extend traditional two frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speed-up over existing methods while retaining a high level of accuracy. In this paper, we show how we can estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function [abstract truncated by publisher]. In this paper, we show how we can estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D rarr 3D rarr 2D transformation. This also allows us to extend traditional two-frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speedup over existing methods while retaining a high level of accuracy. In this paper, we show how to estimate, accurately and efficiently, the 3D motion of a rigid object and time-varying lighting in a dynamic scene. This is achieved in an inverse compositional tracking framework with a novel warping function that involves a 2D --> 3D --> 2D transformation. This also allows us to extend traditional two frame inverse compositional tracking to a sequence of frames, leading to even higher computational savings. We prove the theoretical convergence of this method and show that it leads to significant reduction in computational burden. Experimental analysis on multiple video sequences shows impressive speed-up over existing methods while retaining a high level of accuracy. |
| Author | Yilei Xu Roy-Chowdhury, A.K. |
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| Keywords | Motion Video analysis Inverse composition Tracking Image processing Warping High precision Posture Multiple view Time varying system Image analysis Luminance Scene analysis Lighting Illumination Pattern analysis Artificial intelligence Numerical convergence 3D pose |
| Language | English |
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| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Cameras Computation Computer science; control theory; systems Convergence Dynamics Exact sciences and technology Illumination Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Imaging, Three-Dimensional - methods Integrated circuit modeling Inverse Layout Lighting Lighting - methods Motion Motion estimation Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Reflectivity Reproducibility of Results Sensitivity and Specificity Shape Target tracking Three dimensional Tracking Two dimensional Video analysis Video Recording - methods Video sequences |
| Title | Inverse Compositional Estimation of 3D Pose And Lighting in Dynamic Scenes |
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