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Volumn 9910 LNCS, Issue , 2016, Pages 484-499

Learning a predictable and generative vector representation for objects

Author keywords

[No Author keywords available]

Indexed keywords

3D MODELING; EMBEDDINGS;

EID: 84990019747     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46466-4_29     Document Type: Conference Paper
Times cited : (562)

References (40)
  • 1
    • 84911409986 scopus 로고    scopus 로고
    • Seeing 3D chairs: Exemplar part-based 2D-3D alignment using a large dataset of cad models
    • Aubry, M., Maturana, D., Efros, A., Russell, B., Sivic, J.: Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of cad models. In: CVPR (2014)
    • (2014) CVPR
    • Aubry, M.1    Maturana, D.2    Efros, A.3    Russell, B.4    Sivic, J.5
  • 4
    • 85198028989 scopus 로고    scopus 로고
    • Imagenet: A large-scale hierarchical image database
    • Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248-255 (2009)
    • (2009) CVPR , pp. 248-255
    • Deng, J.1    Dong, W.2    Socher, R.3    Li, L.J.4    Li, K.5    Fei-Fei, L.6
  • 5
    • 84959184995 scopus 로고    scopus 로고
    • Learning to generate chairs with convolutional neural networks
    • Dosovitskiy, A., Springenberg, J., Brox, T.: Learning to generate chairs with convolutional neural networks. In: CVPR (2015)
    • (2015) CVPR
    • Dosovitskiy, A.1    Springenberg, J.2    Brox, T.3
  • 6
    • 84973897611 scopus 로고    scopus 로고
    • Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
    • Eigen, D., Fergus, R.: 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
  • 7
    • 84937943470 scopus 로고    scopus 로고
    • Depth map prediction from a single image using a multi-scale deep network
    • Eigen, D., Puhrsch, C., Fergus, R.: 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
  • 8
    • 84898832490 scopus 로고    scopus 로고
    • Data-driven 3D primitives for single image understanding
    • Fouhey, D.F., Gupta, A., Hebert, M.: Data-driven 3D primitives for single image understanding. In: ICCV (2013)
    • (2013) ICCV
    • Fouhey, D.F.1    Gupta, A.2    Hebert, M.3
  • 12
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504-507 (2006)
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 13
    • 34547216923 scopus 로고    scopus 로고
    • Recovering surface layout from an image
    • Hoiem, D., Efros, A.A., Hebert, M.: Recovering surface layout from an image. IJCV 75(1), 151-172 (2007)
    • (2007) IJCV , vol.75 , Issue.1 , pp. 151-172
    • Hoiem, D.1    Efros, A.A.2    Hebert, M.3
  • 14
    • 84943786323 scopus 로고    scopus 로고
    • Single-view reconstruction via joint analysis of image and shape collections
    • Huang, Q., Wang, H., Koltun, V.: Single-view reconstruction via joint analysis of image and shape collections. In: SIGGRAPH, vol. 34, no. 4 (2015)
    • (2015) SIGGRAPH , vol.34 , Issue.4
    • Huang, Q.1    Wang, H.2    Koltun, V.3
  • 17
    • 84959254233 scopus 로고    scopus 로고
    • Category-specific object reconstruction from a single image
    • Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Category-specific object reconstruction from a single image. In: CVPR (2015)
    • (2015) CVPR
    • Kar, A.1    Tulsiani, S.2    Carreira, J.3    Malik, J.4
  • 18
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097-1105 (2012)
    • (2012) NIPS , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 21
    • 84995745188 scopus 로고    scopus 로고
    • Joint embeddings of shapes and images via cnn image purification
    • Li, Y., Su, H., Qi, C.R., Fish, N., Cohen-Or, D., Guibas, L.J.: Joint embeddings of shapes and images via cnn image purification. ACM TOG 34(6), 1-12 (2015)
    • (2015) ACM TOG , vol.34 , Issue.6 , pp. 1-12
    • Li, Y.1    Su, H.2    Qi, C.R.3    Fish, N.4    Cohen-Or, D.5    Guibas, L.J.6
  • 22
    • 84906350535 scopus 로고    scopus 로고
    • FPM: Fine pose parts-based model with 3D CAD models
    • Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Heidelberg
    • Lim, J.J., Khosla, A., Torralba, A.: FPM: fine pose parts-based model with 3D CAD models. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 478-493. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_31
    • (2014) ECCV 2014. LNCS , vol.8694 , pp. 478-493
    • Lim, J.J.1    Khosla, A.2    Torralba, A.3
  • 23
    • 84898778816 scopus 로고    scopus 로고
    • Parsing IKEA objects: Fine pose estimation
    • Lim, J.J., Pirsiavash, H., Torralba, A.: Parsing IKEA objects: fine pose estimation. In: ICCV (2013)
    • (2013) ICCV
    • Lim, J.J.1    Pirsiavash, H.2    Torralba, A.3
  • 24
    • 84958159870 scopus 로고    scopus 로고
    • VoxNet: A 3D convolutional neural network for real-time object recognition
    • Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: IROS (2015)
    • (2015) IROS
    • Maturana, D.1    Scherer, S.2
  • 25
    • 84898956512 scopus 로고    scopus 로고
    • Distributed representations of words and phrases and their compositionality
    • Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)
    • (2013) NIPS
    • Mikolov, T.1    Sutskever, I.2    Chen, K.3    Corrado, G.4    Dean, J.5
  • 26
    • 0029938380 scopus 로고    scopus 로고
    • Emergence of simple-cell receptive field properties by learning a sparse code for natural images
    • Olshausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583), 607-609 (1996)
    • (1996) Nature , vol.381 , Issue.6583 , pp. 607-609
    • Olshausen, B.1    Field, D.2
  • 31
    • 64849095075 scopus 로고    scopus 로고
    • Make3D: Learning 3D scene structure from a single still image
    • Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3D scene structure from a single still image. TPAMI 30(5), 824-840 (2008)
    • (2008) TPAMI , vol.30 , Issue.5 , pp. 824-840
    • Saxena, A.1    Sun, M.2    Ng, A.Y.3
  • 33
    • 84898454739 scopus 로고    scopus 로고
    • Back to the future: Learning shape models from 3D CAD data
    • Stark, M., Goesele, M., Schiele, B.: Back to the future: learning shape models from 3D CAD data. In: BMVC (2010)
    • (2010) BMVC
    • Stark, M.1    Goesele, M.2    Schiele, B.3
  • 34
    • 84973882748 scopus 로고    scopus 로고
    • Multi-view convolutional neural networks for 3d shape recognition
    • Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3d shape recognition. In: ICCV (2015)
    • (2015) ICCV
    • Su, H.1    Maji, S.2    Kalogerakis, E.3    Learned-Miller, E.G.4
  • 35
    • 84973860892 scopus 로고    scopus 로고
    • Render for CNN: Viewpoint estimation in images using CNNs trained with rendered 3D model views
    • Su, H., Qi, C.R., Li, Y., Guibas, L.J.: Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views. In: ICCV (2015)
    • (2015) ICCV
    • Su, H.1    Qi, C.R.2    Li, Y.3    Guibas, L.J.4
  • 36
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
    • Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 625-660 (2010)
    • (2010) J. Mach. Learn. Res , vol.11 , pp. 625-660
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.A.5
  • 37
    • 84959234840 scopus 로고    scopus 로고
    • Designing deep networks for surface normal estimation
    • Wang, X., Fouhey, D.F., Gupta, A.: Designing deep networks for surface normal estimation. In: CVPR (2015)
    • (2015) CVPR
    • Wang, X.1    Fouhey, D.F.2    Gupta, A.3
  • 38
    • 84990046297 scopus 로고    scopus 로고
    • Single image 3d interpreter network
    • Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.), Springer, Heidelberg
    • Wu, J., Xue, T., Lim, J.J., Tian, Y., Tenenbaum, J.B., Torralba, A., Freeman, W.T.: Single image 3d interpreter network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part VI. LNCS, vol. 9910, pp. 365-382. Springer, Heidelberg (2016)
    • (2016) ECCV 2016, Part VI. LNCS , vol.9910 , pp. 365-382
    • Wu, J.1    Xue, T.2    Lim, J.J.3    Tian, Y.4    Tenenbaum, J.B.5    Torralba, A.6    Freeman, W.T.7
  • 39
    • 84949636429 scopus 로고    scopus 로고
    • Xiao, J.: 3D shapenets: A deep representation for volumetric shapes
    • Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3D shapenets: a deep representation for volumetric shapes. In: CVPR (2015)
    • (2015) CVPR
    • Wu, Z.1    Song, S.2    Khosla, A.3    Yu, F.4    Zhang, L.5    Tang, X.6
  • 40
    • 84904687911 scopus 로고    scopus 로고
    • Beyond pascal: A benchmark for 3d object detection in the wild
    • Xiang, Y., Mottaghi, R., Savarese, S.: Beyond pascal: a benchmark for 3d object detection in the wild. In: WACV (2014)
    • (2014) WACV
    • Xiang, Y.1    Mottaghi, R.2    Savarese, S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.