메뉴 건너뛰기




Volumn 2016-December, Issue , 2016, Pages 4508-4516

Deep reflectance maps

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; INVERSE PROBLEMS;

EID: 84986317476     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.488     Document Type: Conference Paper
Times cited : (115)

References (39)
  • 1
    • 84959184995 scopus 로고    scopus 로고
    • Learning to generate chairs with convolutional neural networks
    • A.Dosovitskiy, J.T.Springenberg, and T.Brox. Learning to generate chairs with convolutional neural networks. In CVPR, 2015.
    • (2015) CVPR
    • Dosovitskiy, A.1    Springenberg, J.T.2    Brox, T.3
  • 2
    • 84947217753 scopus 로고    scopus 로고
    • Shape, illumination, and reflectance from shading
    • J. T. Barron and J. Malik. Shape, illumination, and reflectance from shading. PAMI, 2015.
    • (2015) PAMI
    • Barron, J.T.1    Malik, J.2
  • 3
    • 0002309377 scopus 로고
    • Recovering intrinsic scene characteristics from images
    • H. G. Barrow and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. Comp. Vis. Sys., 1978.
    • (1978) Comp. Vis. Sys.
    • Barrow, H.G.1    Tenenbaum, J.M.2
  • 6
    • 0031634414 scopus 로고    scopus 로고
    • Rendering synthetic objects into real scenes: Bridging traditional and image-based graphics with global illumination and high dynamic range photography
    • P. Debevec. Rendering synthetic objects into real scenes: Bridging traditional and image-based graphics with global illumination and high dynamic range photography. SIGGRAPH, 1998.
    • (1998) SIGGRAPH
    • Debevec, P.1
  • 8
    • 84973897611 scopus 로고    scopus 로고
    • Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
    • D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In ICCV, 2015.
    • (2015) ICCV
    • Eigen, D.1    Fergus, R.2
  • 9
    • 84937943470 scopus 로고    scopus 로고
    • Depth map prediction from a single image using a multi-scale deep network
    • D. Eigen, C. Puhrsch, and R. Fergus. Depth map prediction from a single image using a multi-scale deep network. In NIPS, 2014.
    • (2014) NIPS
    • Eigen, D.1    Puhrsch, C.2    Fergus, R.3
  • 10
    • 84911400494 scopus 로고    scopus 로고
    • Rich feature hierarchies for accurate object detection and semantic segmentation
    • R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
    • (2014) CVPR
    • Girshick, R.B.1    Donahue, J.2    Darrell, T.3    Malik, J.4
  • 12
    • 84959236250 scopus 로고    scopus 로고
    • Hypercolumns for object segmentation and fine-grained localization
    • B. Hariharana, P. Arbelaez, R. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In CVPR, 2015.
    • (2015) CVPR
    • Hariharana, B.1    Arbelaez, P.2    Girshick, R.3    Malik, J.4
  • 13
    • 24344435585 scopus 로고    scopus 로고
    • Example-based photometric stereo: Shape reconstruction with general, varying brdfs
    • A. Hertzmann and S. M. Seitz. Example-based photometric stereo: Shape reconstruction with general, varying BRDFs. PAMI, 27(8), 2005.
    • (2005) PAMI , vol.27 , Issue.8
    • Hertzmann, A.1    Seitz, S.M.2
  • 14
    • 0018483242 scopus 로고
    • Calculating the reflectance map
    • B. K. Horn and R. W. Sjoberg. Calculating the reflectance map. App. Opt., 18(11), 1979.
    • (1979) App. Opt. , vol.18 , Issue.11
    • Horn, B.K.1    Sjoberg, R.W.2
  • 15
    • 80052884728 scopus 로고    scopus 로고
    • Shape estimation in natural illumination
    • M. K. Johnson and E. H. Adelson. Shape estimation in natural illumination. In CVPR, 2011.
    • (2011) CVPR
    • Johnson, M.K.1    Adelson, E.H.2
  • 18
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS. 2012.
    • (2012) NIPS.
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 20
    • 71149119164 scopus 로고    scopus 로고
    • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    • H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proc. ICML, 2009.
    • (2009) Proc. ICML
    • Lee, H.1    Grosse, R.2    Ranganath, R.3    Ng, A.Y.4
  • 21
    • 84952783215 scopus 로고    scopus 로고
    • Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
    • B. Li, C. Shen, Y. Dai, A. van den Hengel, and M. He. Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs. In CVPR, 2015.
    • (2015) CVPR
    • Li, B.1    Shen, C.2    Dai, Y.3    Hengel Den A.Van4    He, M.5
  • 23
    • 84959229072 scopus 로고    scopus 로고
    • Deep convolutional neural fields for depth estimation from a single image
    • F. Liu, C. Shen, and G. Lin. Deep convolutional neural fields for depth estimation from a single image. In CVPR, 2015.
    • (2015) CVPR
    • Liu, F.1    Shen, C.2    Lin, G.3
  • 24
    • 85009890120 scopus 로고    scopus 로고
    • Reflectance and illumination recovery in the wild
    • S. Lombardi and K. Nishino. Reflectance and illumination recovery in the wild. PAMI, 2015.
    • (2015) PAMI
    • Lombardi, S.1    Nishino, K.2
  • 25
    • 84959205572 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015.
    • (2015) CVPR
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 27
    • 84973882820 scopus 로고    scopus 로고
    • Direct intrinsics: Learning albedo-shading decomposition by convolutional regression
    • T. Narihira, M. Maire, and S. X. Yu. Direct intrinsics: Learning albedo-shading decomposition by convolutional regression. In ICCV, 2015.
    • (2015) ICCV
    • Narihira, T.1    Maire, M.2    Yu, S.X.3
  • 28
    • 84959199929 scopus 로고    scopus 로고
    • Learning lightness from human judgement on relative reflectance
    • T. Narihira, M. Maire, and S. X. Yu. Learning lightness from human judgement on relative reflectance. In CVPR, 2015.
    • (2015) CVPR
    • Narihira, T.1    Maire, M.2    Yu, S.X.3
  • 30
    • 84911430625 scopus 로고    scopus 로고
    • Imagebased synthesis and re-synthesis of viewpoints guided by 3d models
    • K. Rematas, T. Ritschel, M. Fritz, and T. Tuytelaars. Imagebased synthesis and re-synthesis of viewpoints guided by 3d models. In CVPR, 2014.
    • (2014) CVPR
    • Rematas, K.1    Ritschel, T.2    Fritz, M.3    Tuytelaars, T.4
  • 31
    • 84959253210 scopus 로고    scopus 로고
    • Discriminative shape from shading in uncalibrated illumination
    • S. Richter and S.Roth. Discriminative shape from shading in uncalibrated illumination. In CVPR, 2015.
    • (2015) CVPR
    • Richter, S.1    Roth, S.2
  • 32
    • 85009855737 scopus 로고    scopus 로고
    • Right Hemisphere
    • Right Hemisphere. ZBruhs MatCap, 2015.
    • (2015) ZBruhs MatCap
  • 35
    • 84959234840 scopus 로고    scopus 로고
    • Designing deep networks for surface normal estimation
    • X. Wang, D. F. Fouhey, and A. Gupta. Designing deep networks for surface normal estimation. In CVPR, 2015.
    • (2015) CVPR
    • Wang, X.1    Fouhey, D.F.2    Gupta, A.3
  • 38
    • 84973884441 scopus 로고    scopus 로고
    • Learning data-driven reflectance priors for intrinsic image decomposition
    • T. Zhou, P. Krähenbühl, and A. Efros. Learning data-driven reflectance priors for intrinsic image decomposition. In ICCV, 2015.
    • (2015) ICCV
    • Zhou, T.1    Krähenbühl, P.2    Efros, A.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.