A simple model for intrinsic image decomposition with depth cues

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2013, Pages 241-248

  Chen, Qifeng ^a   Koltun, Vladlen ^a,b  

^a STANFORD UNIVERSITY   (United States)

^b ADOBE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

DEPTH CUE; INTRINSIC IMAGE DECOMPOSITIONS; MODEL ESTIMATES; NONLOCAL; NUMBER OF COMPONENTS; REAL-WORLD IMAGE; SIMPLE MODELING;

COLOR IMAGE PROCESSING;

EID: 84898804760     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2013.37     Document Type: Conference Paper

References (36)

1. J. Arvo. The irradiance Jacobian for partially occluded polyhedral sources. In SIGGRAPH, 1994.
2. J. T. Barron and J. Malik. High-frequency shape and albedo from shading using natural image statistics. In CVPR, 2011.
3. J. T. Barron and J. Malik. Color constancy, intrinsic images, and shape estimation. In ECCV, 2012.
4. J. T. Barron and J. Malik. Shape, albedo, and illumination from a single image of an unknown object. In CVPR, 2012.
5. J. T. Barron and J. Malik. Intrinsic scene properties from a single RGB-D image. In CVPR, 2013.
6. H. G. Barrow and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. In Computer Vision Systems. 1978.
7. R. Basri and D.W. Jacobs. Lambertian reflectance and linear subspaces. PAMI, 25(2), 2003.
8. M. Bell and W. T. Freeman. Learning local evidence for shading and reflectance. In ICCV, 2001.
9. A. Blake. Boundary conditions for lightness computation in Mondrian world. Computer Vision, Graphics, and Image Processing, 32(3), 1985.
10. A. Bousseau, S. Paris, and F. Durand. User-assisted intrinsic images. ACM Trans. Graph., 28(5), 2009.
11. D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In ECCV, 2012.
12. G. D. Finlayson, M. S. Drew, and C. Lu. Entropy minimization for shadow removal. IJCV, 85(1), 2009.
13. G. D. Finlayson, S. D. Hordley, C. Lu, and M. S. Drew. On the removal of shadows from images. PAMI, 28(1), 2006.
14. B. V. Funt, M. S. Drew, and M. Brockington. Recovering shading from color images. In ECCV, 1992.
15. E. Garces, A. Muñoz, J. Lopez-Moreno, and D. Gutierrez. Intrinsic images by clustering. Comput. Graph. Forum, 31(4), 2012.
16. P. V. Gehler, C. Rother, M. Kiefel, L. Zhang, and B. Scḧolkopf. Recovering intrinsic images with a global sparsity prior on reflectance. In NIPS, 2011.
17. R. Grosse, M. K. Johnson, E. H. Adelson, and W. T. Freeman. Ground truth dataset and baseline evaluations for intrinsic image algorithms. In ICCV, 2009.
18. B. K. Horn. Determining lightness from an image. Computer Graphics and Image Processing, 3(4), 1974.
19. P.-Y. Laffont, A. Bousseau, and G. Drettakis. Rich intrinsic image decomposition of outdoor scenes from multiple views. IEEE Trans. Vis. Comput. Graph., 19(2), 2013.
20. E. H. Land and J. J. McCann. Lightness and retinex theory. Journal of the Optical Society of America, 61(1), 1971.
21. K. J. Lee, Q. Zhao, X. Tong, M. Gong, S. Izadi, S. U. Lee, P. Tan, and S. Lin. Estimation of intrinsic image sequences from image+depth video. In ECCV, 2012.
22. X. Liu, L. Wan, Y. Qu, T.-T. Wong, S. Lin, C.-S. Leung, and P.-A. Heng. Intrinsic colorization. ACM Trans. Graph., 27(5), 2008.
23. Y. Matsushita, S. Lin, S. B. Kang, and H.-Y. Shum. Estimating intrinsic images from image sequences with biased illumination. In ECCV, 2004.
24. Y. Matsushita, K. Nishino, K. Ikeuchi, and M. Sakauchi. Illumination normalization with time-dependent intrinsic images for video surveillance. PAMI, 26(10), 2004.
25. R. Ramamoorthi and P. Hanrahan. An efficient representation for irradiance environment maps. In SIGGRAPH, 2001.
26. R. Ramamoorthi, D. Mahajan, and P. N. Belhumeur. A first-order analysis of lighting, shading, and shadows. ACM Trans. Graph., 26(1), 2007.
27. J. Shen, X. Yang, Y. Jia, and X. Li. Intrinsic images using optimization. In CVPR, 2011.
28. L. Shen and C. Yeo. Intrinsic images decomposition using a local and global sparse representation of reflectance. In CVPR, 2011.
29. N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from RGBD images. In ECCV, 2012.
30. M. F. Tappen, E. H. Adelson, andW. T. Freeman. Estimating intrinsic component images using non-linear regression. In CVPR, 2006.
31. M. F. Tappen,W. T. Freeman, and E. H. Adelson. Recovering intrinsic images from a single image. PAMI, 27(9), 2005.
32. A. Troccoli and P. K. Allen. Building illumination coherent 3D models of large-scale outdoor scenes. IJCV, 78(2-3), 2008.
33. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 2004.
34. Y. Weiss. Deriving intrinsic images from image sequences. In ICCV, 2001.
35. L.-F. Yu, S.-K. Yeung, Y.-W. Tai, and S. Lin. Shading-based shape refinement of RGB-D images. In CVPR, 2013.
36. Q. Zhao, P. Tan, Q. Dai, L. Shen, E. Wu, and S. Lin. A closed-form solution to retinex with nonlocal texture constraints. PAMI, 34(7), 2012.