메뉴 건너뛰기




Volumn 2016-December, Issue , 2016, Pages 3611-3619

HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

Author keywords

[No Author keywords available]

Indexed keywords

AERIAL PHOTOGRAPHY; CAMERAS; COMPUTER VISION; IMAGE PROCESSING; IMAGE SEGMENTATION; PATTERN RECOGNITION; ROADS AND STREETS;

EID: 84986321469     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.393     Document Type: Conference Paper
Times cited : (161)

References (27)
  • 1
    • 85009936313 scopus 로고    scopus 로고
    • http://fortune.com/2015/10/16/how-tesla-autopilot-learns/.
  • 2
    • 0030190699 scopus 로고    scopus 로고
    • Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation
    • M. Barzohar and D. Cooper. Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. PAMI, 1996.
    • (1996) PAMI
    • Barzohar, M.1    Cooper, D.2
  • 3
    • 84887334515 scopus 로고    scopus 로고
    • Lost! leveraging the crowd for probabilistic visual self-localization
    • M. A. Brubaker, A. Geiger, and R. Urtasun. Lost! leveraging the crowd for probabilistic visual self-localization. In CVPR, 2013.
    • (2013) CVPR
    • Brubaker, M.A.1    Geiger, A.2    Urtasun, R.3
  • 4
    • 84887400264 scopus 로고    scopus 로고
    • Recovering linenetworks in images by junction-point processes
    • D. Chai, W. Forstner, and F. Lafarge. Recovering linenetworks in images by junction-point processes. In CVPR, 2013.
    • (2013) CVPR
    • Chai, D.1    Forstner, W.2    Lafarge, F.3
  • 6
    • 84898820142 scopus 로고    scopus 로고
    • Structured forests for fast edge detection
    • P. Dollár and C. L. Zitnick. Structured forests for fast edge detection. In ICCV, 2013.
    • (2013) ICCV
    • Dollár, P.1    Zitnick, C.L.2
  • 7
    • 84893806308 scopus 로고    scopus 로고
    • Air-ground localization and map augmentation using monocular dense reconstruction
    • C. Forster, M. Pizzoli, and D. Scaramuzza. Air-ground localization and map augmentation using monocular dense reconstruction. In IROS, 2013.
    • (2013) IROS
    • Forster, C.1    Pizzoli, M.2    Scaramuzza, D.3
  • 10
    • 84887356836 scopus 로고    scopus 로고
    • Cross-view image geolocalization
    • T.-Y. Lin, S. Belongie, and J. Hays. Cross-view image geolocalization. In CVPR, 2013.
    • (2013) CVPR
    • Lin, T.-Y.1    Belongie, S.2    Hays, J.3
  • 11
    • 84959245070 scopus 로고    scopus 로고
    • Learning deep representations for ground-to-aerial geolocalization
    • T.-Y. Lin, Y. Cui, S. Belongie, and J. Hays. Learning deep representations for ground-to-aerial geolocalization. In CVPR, 2015.
    • (2015) CVPR
    • Lin, T.-Y.1    Cui, Y.2    Belongie, S.3    Hays, J.4
  • 12
    • 84973867698 scopus 로고    scopus 로고
    • Enhancing road maps by parsing aerial images around the world
    • G. Mattyus, S. Wang, S. Fidler, and R. Urtasun. Enhancing road maps by parsing aerial images around the world. In ICCV, 2015.
    • (2015) ICCV
    • Mattyus, G.1    Wang, S.2    Fidler, S.3    Urtasun, R.4
  • 13
    • 84898804605 scopus 로고    scopus 로고
    • Nyc3dcars: A dataset of 3d vehicles in geographic context
    • K. Matzen and N. Snavely. Nyc3dcars: A dataset of 3d vehicles in geographic context. In ICCV, 2013.
    • (2013) ICCV
    • Matzen, K.1    Snavely, N.2
  • 15
    • 84055175817 scopus 로고    scopus 로고
    • Learning to detect roads in highresolution aerial images
    • V. Mnih and G. E. Hinton. Learning to detect roads in highresolution aerial images. In ECCV, 2010.
    • (2010) ECCV
    • Mnih, V.1    Hinton, G.E.2
  • 16
    • 84867136367 scopus 로고    scopus 로고
    • Learning to label aerial images from noisy data
    • V. Mnih and G. E. Hinton. Learning to label aerial images from noisy data. In ICML, 2012.
    • (2012) ICML
    • Mnih, V.1    Hinton, G.E.2
  • 17
    • 77951294292 scopus 로고    scopus 로고
    • A hierarchical and contextual model for aerial image parsing
    • J. Porway and Q. W. ands Song Chun Zhu. A hierarchical and contextual model for aerial image parsing. IJCV, 88(2):254-283, 2009.
    • (2009) IJCV , vol.88 , Issue.2 , pp. 254-283
    • Porway, J.1    Song, C.2    Zhu, Q.W.3
  • 18
    • 84898804709 scopus 로고    scopus 로고
    • Box in the box: Joint 3d layout and object reasoning from single images
    • A. G. Schwing, S. Fidler, M. Pollefeys, and R. Urtasun. Box In the Box: Joint 3D Layout and Object Reasoning from Single Images. In ICCV, 2013.
    • (2013) ICCV
    • Schwing, A.G.1    Fidler, S.2    Pollefeys, M.3    Urtasun, R.4
  • 20
    • 84872782924 scopus 로고    scopus 로고
    • Exploiting publicly available cartographic resources for aerial image analysis
    • Y.-W. Seo, C. Urmson, and D. Wettergreen. Exploiting publicly available cartographic resources for aerial image analysis. In SIGSPATIAL, 2012.
    • (2012) SIGSPATIAL
    • Seo, Y.-W.1    Urmson, C.2    Wettergreen, D.3
  • 24
    • 24944537843 scopus 로고    scopus 로고
    • Large margin methods for structured and interdependent output variables
    • I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 2005.
    • (2005) JMLR
    • Tsochantaridis, I.1    Joachims, T.2    Hofmann, T.3    Altun, Y.4
  • 25
    • 84959241859 scopus 로고    scopus 로고
    • Holistic 3d scene understanding from a single geo-tagged image
    • S. Wang, S. Fidler, and R. Urtasun. Holistic 3d scene understanding from a single geo-tagged image. CVPR, 2015.
    • (2015) CVPR
    • Wang, S.1    Fidler, S.2    Urtasun, R.3
  • 27
    • 84885223296 scopus 로고    scopus 로고
    • Road segmentation in aerial images by exploiting road vector data
    • J. Yuan and A. Cheriyadat. Road segmentation in aerial images by exploiting road vector data. In COM.geo, 2013.
    • (2013) COM.geo
    • Yuan, J.1    Cheriyadat, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.