9.11.4.1 Intrinsic Image Analysis, Intrinsic Image Decomposition

Matsushita, Y., Nishino, K.[Ko], Ikeuchi, K., Sakauchi, M.,
Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance,
PAMI(26), No. 10, October 2004, pp. 1336-1347.
IEEE Abstract. 0409
BibRef
Earlier: CVPR03(I: 3-10).
IEEE DOI 0307
BibRef

Matsushita, Y., Nishino, K.[Ko], Ikeuchi, K., Sakauchi, M.,
Shadow elimination for robust video surveillance,
Motion02(15-21).
IEEE DOI 0303
BibRef

Matsushita, Y.[Yasuyuki], Lin, S.[Stephen], Shum, H.Y.[Heung-Yeung], Tong, X.[Xin], Kang, S.B.[Sing Bing],
Lighting and Shadow Interpolation Using Intrinsic Lumigraphs,
IJIG(4), No. 4, October 2004, pp. 585-604. 0410
BibRef

Shen, L.[Li], Tan, P.[Ping], Lin, S.[Stephen],
Intrinsic image decomposition with non-local texture cues,
CVPR08(1-7).
IEEE DOI 0806
BibRef

Fusiello, A.[Andrea], Farenzena, M.[Michela], Busti, A., Benedetti, A.,
Computing rigorous bounds to the accuracy of calibrated stereo reconstruction,
VISP(152), No. 6, December 2005, pp. 695-701. 0512
BibRef
Earlier: A2, A3, A1, A4:
Rigorous accuracy bounds for calibrated stereo reconstruction,
ICPR04(IV: 288-292).
IEEE DOI 0409
BibRef

Farenzena, M.[Michela], Dovier, A.[Agostino],
Reconstruction with Interval Constraints Propagation,
CVPR06(I: 1185-1190).
IEEE DOI 0606
BibRef

Farenzena, M.[Michela], Fusiello, A.[Andrea],
Stabilizing 3D modeling with geometric constraints propagation,
CVIU(113), No. 11, November 2009, pp. 1147-1157.
Elsevier DOI 0910
BibRef
Earlier:
3D surface models by geometric constraints propagation,
CVPR08(1-8).
IEEE DOI 0806
BibRef
Earlier:
Recovering Intrinsic Images using an Illumination Invariant Image,
ICIP07(III: 485-488).
IEEE DOI 0709
3D modeling; Geometric constraints BibRef

Tappen, M.F.[Marshall F.], Freeman, W.T.[William T.], Adelson, E.H.[Edward H.],
Recovering Intrinsic Images from a Single Image,
PAMI(27), No. 9, September 2005, pp. 1459-1472.
IEEE DOI 0508
BibRef
Earlier: MIT AIMAIM-2002-015, September 2002.
WWW Link. 0306
Given color and lighting direction, recover shading and reflection. BibRef

Tappen, M.F.[Marshall F.], Adelson, E.H.[Edward H.], Freeman, W.T.[William T.],
Estimating Intrinsic Component Images using Non-Linear Regression,
CVPR06(II: 1992-1999).
IEEE DOI 0606
BibRef

Tappen, M.F.[Marshall F.],
Recovering shape from a single image of a mirrored surface from curvature constraints,
CVPR11(2545-2552).
IEEE DOI 1106
BibRef

Khan, N.[Nazar], Tran, L.[Lam], Tappen, M.F.[Marshall F.],
Training many-parameter shape-from-shading models using a surface database,
3DIM09(1433-1440).
IEEE DOI 0910
BibRef

Johnson, M.K.[Micah K.], Adelson, E.H.[Edward H.],
Shape estimation in natural illumination,
CVPR11(2553-2560).
IEEE DOI 1106
BibRef

Johnson, M.K.[Micah K.], Adelson, E.H.[Edward H.],
Retrographic sensing for the measurement of surface texture and shape,
CVPR09(1070-1077).
IEEE DOI 0906
Device for 2.5D scanner. Haptic, photometric stereo. flecks of paint in clear flexible surface, image from the other side. Very detailed 3D no matter the surface. BibRef

Zoran, D., Isola, P.[Phillip], Krishnan, D., Freeman, W.T.[William T.],
Learning Ordinal Relationships for Mid-Level Vision,
ICCV15(388-396)
IEEE DOI 1602
Context. Pairwise relationships. Not metric properties. BibRef

Cole, F.[Forrester], Isola, P.[Phillip], Freeman, W.T.[William T.], Durand, F.[Frédo], Adelson, E.H.[Edward H.],
Shapecollage: Occlusion-Aware, Example-Based Shape Interpretation,
ECCV12(III: 665-678).
Springer DOI 1210
and line drawings BibRef

Johnson, M.K.[Micah K.], Cole, F.[Forrester], Raj, A.[Alvin], Adelson, E.H.[Edward H.],
Microgeometry Capture using an Elastomeric Sensor,
SIGGraph11(xx-yy).
PDF File. 1109
System, GelSight. More details based on earlier retrographic sensing. The GelSight System. BibRef

Shen, L.[Li], Yeo, C.H.[Chuo-Hao], Hua, B.S.[Binh-Son],
Intrinsic Image Decomposition Using a Sparse Representation of Reflectance,
PAMI(35), No. 12, 2013, pp. 2904-2915.
IEEE DOI 1311
BibRef
Earlier: A1, A2, Only:
Intrinsic images decomposition using a local and global sparse representation of reflectance,
CVPR11(697-704).
IEEE DOI 1106
Image color analysis BibRef

Shen, J., Yang, X., Li, X., Jia, Y.,
Intrinsic Image Decomposition Using Optimization and User Scribbles,
Cyber(43), No. 2, April 2013, pp. 425-436.
IEEE DOI 1303
BibRef

Barron, J.T.[Jonathan T.], Malik, J.[Jitendra],
Shape, Illumination, and Reflectance from Shading,
PAMI(37), No. 8, August 2015, pp. 1670-1687.
IEEE DOI 1507
BibRef
Earlier:
Shape, albedo, and illumination from a single image of an unknown object,
CVPR12(334-341).
IEEE DOI 1208
BibRef
Earlier:
High-frequency shape and albedo from shading using natural image statistics,
CVPR11(2521-2528).
IEEE DOI 1106
Computer vision BibRef

Barron, J.T.[Jonathan T.], Malik, J.[Jitendra],
Intrinsic Scene Properties from a Single RGB-D Image,
PAMI(38), No. 4, April 2016, pp. 690-703.
IEEE DOI 1603
BibRef
Earlier: CVPR13(17-24)
IEEE DOI 1309
BibRef
And:
Color Constancy, Intrinsic Images, and Shape Estimation,
ECCV12(IV: 57-70).
Springer DOI 1210
Computational modeling BibRef

Barron, J.T.,
Convolutional Color Constancy,
ICCV15(379-387)
IEEE DOI 1602
Cognition BibRef

Kang, X.D.[Xu-Dong], Li, S.T.[Shu-Tao], Fang, L.Y.[Le-Yuan], Benediktsson, J.A.[Jón Atli],
Intrinsic Image Decomposition for Feature Extraction of Hyperspectral Images,
GeoRS(53), No. 4, April 2015, pp. 2241-2253.
IEEE DOI 1502
feature extraction BibRef

Hauagge, D.C.[Daniel C.], Wehrwein, S.[Scott], Bala, K.[Kavita], Snavely, N.[Noah],
Photometric Ambient Occlusion for Intrinsic Image Decomposition,
PAMI(38), No. 4, April 2016, pp. 639-651.
IEEE DOI 1603
BibRef
Earlier:
Photometric Ambient Occlusion,
CVPR13(2515-2522)
IEEE DOI 1309
Cameras. albedo; image stacks; intrinsic images. Stack of images from one viewpoint. ambient occlusion: how much light can reach a point in the scene. BibRef

Kovacs, B.[Balazs], Bell, S.[Sean], Snavely, N.[Noah], Bala, K.[Kavita],
Shading Annotations in the Wild,
CVPR17(850-859)
IEEE DOI 1711
Image color analysis, Image decomposition, Lighting, Shape, Surface acoustic waves, Surface, treatment BibRef

Xing, G.Y.[Guan-Yu], Liu, Y.L.[Yan-Li], Zhang, W.[Wanfa], Ling, H.B.[Hai-Bin],
Light mixture intrinsic image decomposition based on a single RGB-D image,
VC(32), No. 6-8, June 2016, pp. 1013-1023.
Springer DOI 1608
BibRef

Jin, X., Gu, Y.,
Superpixel-Based Intrinsic Image Decomposition of Hyperspectral Images,
GeoRS(55), No. 8, August 2017, pp. 4285-4295.
IEEE DOI 1708
Feature extraction, Hyperspectral imaging, Image segmentation, Lighting, Mathematical model, Matrix decomposition, Hyperspectral image, intrinsic image decomposition (IID), optimization, superpixel BibRef

Yue, H., Yang, J., Sun, X., Wu, F., Hou, C.,
Contrast Enhancement Based on Intrinsic Image Decomposition,
IP(26), No. 8, August 2017, pp. 3981-3994.
IEEE DOI 1707
image colour analysis, image enhancement, HSV space, Split Bregman algorithm, color similarity, computing complexity reduction, contrast enhancement, illumination information, illumination layer, intrinsic image decomposition, piece- wise smoothness constraint, piecewise constant, BibRef

Han, G., Xie, X., Lai, J., Zheng, W.S.,
Learning an Intrinsic Image Decomposer Using Synthesized RGB-D Dataset,
SPLetters(25), No. 6, June 2018, pp. 753-757.
IEEE DOI 1806
feature extraction, image colour analysis, image texture, learning (artificial intelligence), neural nets, intrinsic image BibRef

Jin, X., Gu, Y., Liu, T.,
Intrinsic Image Recovery From Remote Sensing Hyperspectral Images,
GeoRS(57), No. 1, January 2019, pp. 224-238.
IEEE DOI 1901
Hyperspectral imaging, Pigments, Feature extraction, Lighting, Mathematical model, Classification, feature extraction, reflectance BibRef

Tong, G.F.[Guo-Feng], Li, Y.[Yong], Sun, A.[Anan], Wang, Y.B.[Yue-Bin],
Shadow effect weakening based on intrinsic image extraction with effective projection of logarithmic domain for road scene,
SIViP(14), No. 4, June 2020, pp. 683-691.
WWW Link. 2005
BibRef

Krebs, A.[Alexandre], Benezeth, Y.[Yannick], Marzani, F.[Franck],
Intrinsic image decomposition as two independent deconvolution problems,
SP:IC(86), 2020, pp. 115872.
Elsevier DOI 2006
Dichromatic reflection model, Inverse problem, Color constancy BibRef

Baslamisli, A.S.[Anil S.], Liu, Y.[Yang], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Physics-based shading reconstruction for intrinsic image decomposition,
CVIU(205), 2021, pp. 103183.
Elsevier DOI 2103
Intrinsic image decomposition, Shading, Albedo, Invariant image descriptors BibRef

Das, P.[Partha], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Intrinsic image decomposition using physics-based cues and CNNs,
CVIU(223), 2022, pp. 103538.
Elsevier DOI 2210
Physics based vision, Intrinsics image decomposition, Deep learning BibRef

Baslamisli, A.S.[Anil S.], Le, H., Gevers, T.[Theo],
CNN Based Learning Using Reflection and Retinex Models for Intrinsic Image Decomposition,
CVPR18(6674-6683)
IEEE DOI 1812
Lighting, Light sources, Standards, Computational modeling, Convolutional neural networks, Mathematical model BibRef

Li, K.[Kun], Wang, Y.J.[Yu-Jie], Ye, X.C.[Xin-Chen], Yan, C.G.[Cheng-Gang], Yang, J.Y.[Jing-Yu],
Sparse intrinsic decomposition and applications,
SP:IC(95), 2021, pp. 116281.
Elsevier DOI 2106
Intrinsic decomposition, RGB-D, Sparse, Non-local, Depth refinement BibRef

Ma, Y.P.[Yu-Peng], Jiang, X.Y.[Xiao-Yue], Xia, Z.Q.[Zhao-Qiang], Gabbouj, M.[Moncef], Feng, X.Y.[Xiao-Yi],
CasQNet: Intrinsic Image Decomposition Based on Cascaded Quotient Network,
CirSysVideo(31), No. 7, July 2021, pp. 2661-2674.
IEEE DOI 2107
Feature extraction, Image decomposition, Image reconstruction, Task analysis, Lighting, Shape, Image analysis, Intrinsic image, U-net BibRef

Li, W.[Wen], Resmerita, E.[Elena], Vese, L.A.[Luminita A.],
Multiscale Hierarchical Image Decomposition and Refinements: Qualitative and Quantitative Results,
SIIMS(14), No. 2, 2021, pp. 844-877.
DOI Link 2107
BibRef

Baslamisli, A.S.[Anil S.], Das, P.[Partha], Le, H.A.[Hoang-An], Karaoglu, S.[Sezer], Gevers, T.[Theo],
ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition,
IJCV(129), No. 8, August 2021, pp. 2445-2473.
Springer DOI 2108
BibRef

Baslamisli, A.S.[Anil S.], Groenestege, T.T.[Thomas T.], Das, P.[Partha], Le, H.A.[Hoang-An], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Joint Learning of Intrinsic Images and Semantic Segmentation,
ECCV18(VI: 289-305).
Springer DOI 1810
BibRef

Garces, E.[Elena], Rodriguez-Pardo, C.[Carlos], Casas, D.[Dan], Lopez-Moreno, J.[Jorge],
A Survey on Intrinsic Images: Delving Deep into Lambert and Beyond,
IJCV(130), No. 3, March 2022, pp. 836-868.
Springer DOI 2203
Survey, Intrinsic Images. BibRef

Liu, X.Y.[Xiang-Yuan], Wu, Z.K.[Zhong-Ke], Wang, X.C.[Xing-Ce],
A robust intrinsic feature of images derived from the tensor manifold,
PRL(160), 2022, pp. 73-81.
Elsevier DOI 2208
Riemannian manifold, Positive definite symmetric matrices, Tensor, Structure tensor, Geodesic distance BibRef

Forsyth, D.A.[David A.], Rock, J.J.[Jason J.],
Intrinsic Image Decomposition Using Paradigms,
PAMI(44), No. 11, November 2022, pp. 7624-7637.
IEEE DOI 2210
Standards, Computational modeling, Training, Image decomposition, Data models, Training data, Licenses, reflectance, image models, unsupervised learning BibRef

Zhang, Q.[Qing], Zhou, J.[Jin], Zhu, L.[Lei], Sun, W.[Wei], Xiao, C.X.[Chun-Xia], Zheng, W.S.[Wei-Shi],
Unsupervised Intrinsic Image Decomposition Using Internal Self-Similarity Cues,
PAMI(44), No. 12, December 2022, pp. 9669-9686.
IEEE DOI 2212
Training, Lighting, Image reconstruction, Image decomposition, Surface acoustic waves, Image sequences, Annotations, shading BibRef

Zhang, F.[Feng], Jiang, X.Y.[Xiao-Yue], Xia, Z.Q.[Zhao-Qiang], Gabbouj, M.[Moncef], Peng, J.Y.[Jin-Ye], Feng, X.Y.[Xiao-Yi],
Non-Local Color Compensation Network for Intrinsic Image Decomposition,
CirSysVideo(33), No. 1, January 2023, pp. 132-145.
IEEE DOI 2301
Image color analysis, Feature extraction, Image decomposition, Image reconstruction, Lighting, Task analysis, Decoding, mutual constraint BibRef

Li, Y.D.[Yuan-Dong], Hu, Q.L.[Qing-Lei], Ouyang, Z.C.[Zhen-Chao], Shen, S.H.[Shu-Han],
Neural Reflectance Decomposition Under Dynamic Point Light,
CirSysVideo(34), No. 4, April 2024, pp. 2195-2208.
IEEE DOI 2404
Geometry, Surface texture, Rendering (computer graphics), Light sources, Reflectivity, self-supervised BibRef

Yoshida, Y.[Yusaku], Kawahara, R.[Ryo], Okabe, T.[Takahiro],
Light Source Separation and Intrinsic Image Decomposition under AC Illumination,
CVPR23(5735-5743)
IEEE DOI 2309
BibRef

Maralan, S.S.[Sepideh Sarajian], Careaga, C.[Chris], Aksoy, Y.[Yagiz],
Computational Flash Photography through Intrinsics,
CVPR23(16654-16662)
IEEE DOI 2309
BibRef

Das, P.[Partha], Karaoglu, S.[Sezer], Gijsenij, A.[Arjan], Gevers, T.[Theo],
Signet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes,
CVMeta22(605-620).
Springer DOI 2304
BibRef

Sun, H.[Haomiao], Shan, S.G.[Shi-Guang], Han, H.[Hu],
Intrinsic Imaging Model Enhanced Contrastive Face Representation Learning,
FG23(1-8)
IEEE DOI 2303
Representation learning, Training, Solid modeling, Analytical models, Imaging, Data models BibRef

Ulucan, D.[Diclehan], Ulucan, O.[Oguzhan], Ebner, M.[Marc],
IID-NORD: A Comprehensive Intrinsic Image Decomposition Dataset,
ICIP22(2831-2835)
IEEE DOI 2211
Reflectivity, Image segmentation, Shape, Lighting, Benchmark testing, Image decomposition, Intrinsic image decomposition, dataset, computer graphics BibRef

Zhang, F.[Fan], You, S.[Shaodi], Li, Y.[Yu], Fu, Y.[Ying],
HSI-Guided Intrinsic Image Decomposition for Outdoor Scenes,
PBVS22(312-321)
IEEE DOI 2210
Reflectivity, Roads, Surface acoustic waves, Manuals, Rendering (computer graphics), Image decomposition BibRef

Munkberg, J.[Jacob], Chen, W.Z.[Wen-Zheng], Hasselgren, J.[Jon], Evans, A.[Alex], Shen, T.C.[Tian-Chang], Müller, T.[Thomas], Gao, J.[Jun], Fidler, S.[Sanja],
Extracting Triangular 3D Models, Materials, and Lighting From Images,
CVPR22(8270-8280)
IEEE DOI 2210
Graphics, Solid modeling, Computational modeling, Lighting, Rendering (computer graphics), Topology, Vision + graphics BibRef

Das, P.[Partha], Karaoglu, S.[Sezer], Gevers, T.[Theo],
PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition,
CVPR22(19758-19767)
IEEE DOI 2210
Reflectivity, Photography, Deep learning, Codes, Image edge detection, Lighting, Computational photography, Physics-based vision and shape-from-X BibRef

Weligampola, H.[Harshana], Jayatilaka, G.[Gihan], Sritharan, S.[Suren], Ekanayake, P.[Parakrama], Ragel, R.[Roshan], Herath, V.[Vijitha], Godaliyadda, R.[Roshan],
An Optical Physics Inspired CNN Approach for Intrinsic Image Decomposition,
ICIP21(1864-1868)
IEEE DOI 2201
Reflectivity, Neural networks, Optical fiber networks, Optical imaging, Image decomposition, Numerical models, Phong model BibRef

Alhaija, H.A., Mustikovela, S.K., Thies, J., Jampani, V., Nießner, M., Geiger, A., Rother, C.[Carsten],
Intrinsic Autoencoders for Joint Deferred Neural Rendering and Intrinsic Image Decomposition,
3DV20(1176-1185)
IEEE DOI 2102
Rendering (computer graphics), Solid modeling, Image synthesis, Training, Task analysis, Geometry, GAN BibRef

Liu, A.[Andrew], Ginosar, S.[Shiry], Zhou, T.H.[Ting-Hui], Efros, A.A.[Alexei A.], Snavely, N.[Noah],
Learning to Factorize and Relight a City,
ECCV20(IV:544-561).
Springer DOI 2011
BibRef

Zhou, H., Yu, X., Jacobs, D.,
GLoSH: Global-Local Spherical Harmonics for Intrinsic Image Decomposition,
ICCV19(7819-7828)
IEEE DOI 2004
image colour analysis, learning (artificial intelligence), lighting, coarse-to-fine network structure, coarse network, Surface acoustic waves BibRef

Alayrac, J.B.[Jean-Baptiste], Carreira, J.[Joao], Zisserman, A.[Andrew],
The Visual Centrifuge: Model-Free Layered Video Representations,
CVPR19(2452-2461).
IEEE DOI 2002
BibRef

Alayrac, J.B.[Jean-Baptiste], Carreira, J.[Joao], Arandjelovic, R.[Relja], Zisserman, A.[Andrew],
Controllable Attention for Structured Layered Video Decomposition,
ICCV19(5733-5742)
IEEE DOI 2004
Separate reflections, transparency or object motion. feature extraction, image motion analysis, learning (artificial intelligence), neural nets, Controllability BibRef

Cheng, Z.[Ziang], Zheng, Y.Q.[Yin-Qiang], You, S.D.[Shao-Di], Sato, I.[Imari],
Non-Local Intrinsic Decomposition With Near-Infrared Priors,
ICCV19(2521-2530)
IEEE DOI 2004
computational complexity, convolution, image colour analysis, minimisation, Training BibRef

Cheng, L., Zhang, C., Liao, Z.,
Intrinsic Image Transformation via Scale Space Decomposition,
CVPR18(656-665)
IEEE DOI 1812
Laplace equations, Task analysis, Training, Space exploration, Network architecture, Computer architecture, Transforms BibRef

Huang, Q., Zhu, W., Zhao, Y., Chen, L., Wang, Y., Yue, T., Cao, X.,
Multispectral Image Intrinsic Decomposition via Subspace Constraint,
CVPR18(6430-6439)
IEEE DOI 1812
Lighting, Image color analysis, Image segmentation, Geometry, Image decomposition, Subspace constraints, Color BibRef

Fan, Q., Yang, J., Hua, G., Chen, B., Wipf, D.,
Revisiting Deep Intrinsic Image Decompositions,
CVPR18(8944-8952)
IEEE DOI 1812
Training, Image edge detection, Image decomposition, Benchmark testing, Optimization, Training data, Lighting BibRef

Li, Z., Snavely, N.,
Learning Intrinsic Image Decomposition from Watching the World,
CVPR18(9039-9048)
IEEE DOI 1812
Training, Lighting, Videos, Image sequences, Image reconstruction, Sparse matrices BibRef

Han, G., Xie, X., Zheng, W., Lai, J.,
Learning Intrinsic Image Decomposition by Deep Neural Network with Perceptual Loss,
ICPR18(91-96)
IEEE DOI 1812
feature extraction, learning (artificial intelligence), neural nets, Image decomposition BibRef

Li, Z.Q.[Zheng-Qi], Snavely, N.[Noah],
CGIntrinsics: Better Intrinsic Image Decomposition Through Physically-Based Rendering,
ECCV18(III: 381-399).
Springer DOI 1810
BibRef

Garces, E., Reinhard, E.,
Light-Field Surface Color Segmentation with an Application to Intrinsic Decomposition,
WACV18(1480-1488)
IEEE DOI 1806
image colour analysis, image segmentation, dense light fields, fully automatic segmentation pipeline, intrinsic decomposition, Surface treatment BibRef

Muhammad, S.[Siraj], Dailey, M.N.[Matthew N.], Sato, I.[Imari], Majeed, M.F.[Muhammad F.],
Handling Specularity in Intrinsic Image Decomposition,
ICIAR18(107-115).
Springer DOI 1807
BibRef

Nestmeyer, T., Gehler, P.V.,
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation,
CVPR17(1771-1780)
IEEE DOI 1711
Benchmark testing, Estimation, Image color analysis, Image decomposition, Standards BibRef

Kim, S.R.[Seung-Ryong], Park, K.[Kihong], Sohn, K.H.[Kwang-Hoon], Lin, S.[Stephen],
Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields,
ECCV16(VIII: 143-159).
Springer DOI 1611
BibRef

Xie, D.H.[De-Hua], Liu, S.C.[Shuai-Cheng], Lin, K.[Kaimo], Zhu, S.Y.[Shu-Yuan], Zeng, B.[Bing],
Intrinsic decomposition for stereoscopic images,
ICIP16(1744-1748)
IEEE DOI 1610
Avalanche photodiodes. Reflectance and shading. BibRef

Laffont, P.Y., Bazin, J.C.,
Intrinsic Decomposition of Image Sequences from Local Temporal Variations,
ICCV15(433-441)
IEEE DOI 1602
Reflectance and shading. Geometry BibRef

Zhou, T., Krähenbühl, P.[Philipp], Efros, A.A.,
Learning Data-Driven Reflectance Priors for Intrinsic Image Decomposition,
ICCV15(3469-3477)
IEEE DOI 1602
Computer vision BibRef

Narihira, T., Maire, M., Yu, S.X.,
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression,
ICCV15(2992-2992)
IEEE DOI 1602
Color BibRef

Jeon, J.[Junho], Cho, S.H.[Sung-Hyun], Tong, X.[Xin], Lee, S.Y.[Seung-Yong],
Intrinsic Image Decomposition Using Structure-Texture Separation and Surface Normals,
ECCV14(VII: 218-233).
Springer DOI 1408
using RGB-D image. also use texture. BibRef

Shelhamer, E.[Evan], Barron, J.T., Darrell, T.J.[Trevor J.],
Scene Intrinsics and Depth from a Single Image,
IR15(235-242)
IEEE DOI 1602
Cognition; Lighting; Optimization; Pipelines; Sensors; Shape; Training BibRef

Kong, N.[Naejin], Black, M.J.[Michael J.],
Intrinsic Depth: Improving Depth Transfer with Intrinsic Images,
ICCV15(3514-3522)
IEEE DOI 1602
Cameras BibRef

Chang, J.[Jason], Cabezas, R.[Randi], Fisher, III, J.W.[John W.],
Bayesian Nonparametric Intrinsic Image Decomposition,
ECCV14(IV: 704-719).
Springer DOI 1408
BibRef

Kong, N.[Naejin], Gehler, P.V.[Peter V.], Black, M.J.[Michael J.],
Intrinsic Video,
ECCV14(II: 360-375).
Springer DOI 1408
extracting temporally coherent albedo and shading from video alone. BibRef

Chen, Q.F.[Qi-Feng], Koltun, V.[Vladlen],
Photographic Image Synthesis with Cascaded Refinement Networks,
ICCV17(1520-1529)
IEEE DOI 1802
BibRef
Earlier:
Fast MRF Optimization with Application to Depth Reconstruction,
CVPR14(3914-3921)
IEEE DOI 1409
BibRef
Earlier:
A Simple Model for Intrinsic Image Decomposition with Depth Cues,
ICCV13(241-248)
IEEE DOI 1403
feedforward neural nets, image resolution, regression analysis, 2-megapixel resolution, cascaded refinement networks. Depth Reconstruction; MRF Optimization. RGB-D images. estimate albedo and shading fields. BibRef

Yu, J.Z.[Jin-Ze],
Rank-constrained PCA for intrinsic images decomposition,
ICIP16(3578-3582)
IEEE DOI 1610
Computer vision BibRef

Yu, J.Z.[Jin-Ze], Sato, Y.[Yoichi],
Fast sparse edge-based intrinsic image decomposition guided by chromaticity gradients,
ICIP15(3753-3757)
IEEE DOI 1512
L0-norm BibRef

Liu, Y.L.[Yuan-Liu], Yuan, Z.J.[Ze-Jian], Zheng, N.N.[Nan-Ning],
Intrinsic Image Decomposition from Pair-Wise Shading Ordering,
ACCV14(V: 83-98).
Springer DOI 1504
intrinsic images. BibRef

Liao, Z.C.[Zi-Cheng], Rock, J.J.[Jason J.], Wang, Y.[Yang], Forsyth, D.A.[David A.],
Non-parametric Filtering for Geometric Detail Extraction and Material Representation,
CVPR13(963-970)
IEEE DOI 1309
Geometric detail; intrinsic image decomposition; material representation. Separate description of detail from intrinsic image. Coarse shading plus details. Enables editing. BibRef

Kumar, P.,
Intrinsic Image Based Moving Object Cast Shadow Removal in Image Sequences,
DICTA11(410-415).
IEEE DOI 1205
BibRef

Shen, J.B.[Jian-Bing], Yang, X.S.[Xiao-Shan], Jia, Y.D.[Yun-De], Li, X.L.[Xue-Long],
Intrinsic images using optimization,
CVPR11(3481-3487).
IEEE DOI 1106
Similar neighboring colors are similar reflectance properties. BibRef

Grosse, R.[Roger], Johnson, M.K.[Micah K.], Adelson, E.H.[Edward H.], Freeman, W.T.[William T.],
Ground truth dataset and baseline evaluations for intrinsic image algorithms,
ICCV09(2335-2342).
IEEE DOI 0909
Dataset, Shading. For shading and reflectance computations. BibRef

Jiang, X.Y.[Xiao-Yue], Schofield, A.J.[Andrew J.], Wyatt, J.L.[Jeremy L.],
Correlation-Based Intrinsic Image Extraction from a Single Image,
ECCV10(IV: 58-71).
Springer DOI 1009
Luminance. BibRef

El-Melegy, M.T.,
Image Intrinsic Values from Shading Information,
ICIP05(II: 1166-1169).
IEEE DOI 0512
BibRef

Kell, M.S., Cristobal, G., Neumann, H.,
Neural mechanisms for segregation and recovering of intrinsic images features,
ICIP03(I: 693-696).
IEEE DOI 0312
BibRef

Weiss, Y.[Yair],
Deriving Intrinsic Images from Image Sequences,
ICCV01(II: 68-75).
IEEE DOI 0106
Stationary camera, changing sun. Multiple images give the reflectance image separate from the illumination. BibRef

Chapter on 3-D Shape from X -- Shading, Textures, Lasers, Structured Light, Focus, Line Drawings continues in
Dual/Gradient Space Concepts .