使用線段一致性從單張影像估測三維室內曼哈頓場景

張星群

doi:10.6843/NTHU.2014.00636

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使用線段一致性從單張影像估測三維室內曼哈頓場景

Using line consistency to estimate 3D indoor Manhattan scene layout from a single image

張星群 , Masters　　Advisor：賴尚宏　　

英文 DOI： 10.6843/NTHU.2014.00636

三維室內場景；曼哈頓世界假說；線段一致性；成本函數； 3D indoor scene ； Manhattan world assumption ； line consistency ； cost function

Abstract │ Reference (17) │ Altmetrics

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In this thesis, a visual analysis approach based on computer vision algorithm is proposed to estimate the interior layout from single input image. In the proposed system, the interior space can be viewed as a three-dimensional box which includes ceiling, floor, and walls. The regions corresponding to different surfaces can be calculated by projecting the 3D box into two-dimensional image. This thesis utilizes the consistency of coplanar lines and the boundary edges between different surfaces (ceiling, floor, and walls) to design a cost function. The rotation, translation, and deformation parameters of the interior layout can be estimated by a cost function minimization process. In the experimental results, an adaptive weight parameter of the cost function is examined. The prior knowledge of real-world testing image can improve the accuracy dramatically with an intelligent weight adjustment method. Furthermore, a simulation experiment based on large-scale testing data is exploited to prove the robustness and stability of the proposed system. Finally, the proposed system also examined various real testing images, which include new collected data and the indoor scene dataset in public domain, to demonstrate the accuracy and robustness of the proposed method.

Reference ( 17 ) 〈TOP〉

[1] J. Coughlan and A. Yuille. Manhattan world: compass direction from a single image by bayesian inference. In Conference on Computer Vision and Pattern Recognition, volume 2, pages 941-947 vol.2, 1999.
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[3] D. Hoiem, A. A. Efros, and M. Hebert. Recovering surface layout from an image. In International Journal of Computer Vision, 2007.
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[4] Tretiak E, Barinova O, Kohli P, Lempitsky V. Geometric image parsing in man-made environments. In International Journal of Computer Vision, 2011.
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[5] H. Wang, S. Gould, and D. Koller. Discriminative Learning with Latent Variables for Cluttered Indoor Scene Understanding. In European Conference on Computer Vision, 2010.
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[10] L. D. Pero, J. Bowdish, E. L. Hartley, B. Kermgard, K. Barnard. Understanding Bayesian rooms using composite 3D object models. In Conference on Computer Vision and Pattern Recognition, 2013.
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