메뉴 건너뛰기




Volumn 2015-December, Issue , 2015, Pages 922-928

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Author keywords

[No Author keywords available]

Indexed keywords

CONVOLUTION; INTELLIGENT ROBOTS; NEURAL NETWORKS; OPTICAL RADAR; ROBOTICS; ROBOTS;

EID: 84958159870     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2015.7353481     Document Type: Conference Paper
Times cited : (3873)

References (38)
  • 2
    • 84871736327 scopus 로고    scopus 로고
    • Visual odometry and mapping for autonomous flight using an rgb-d camera
    • Flagstaff, Arizona, USA, Aug.
    • A. S. Huang, A. Bachrach, P. Henry, M. Krainin, D. Maturana, D. Fox, and N. Roy, "Visual odometry and mapping for autonomous flight using an rgb-d camera, " in ISRR, Flagstaff, Arizona, USA, Aug. 2011
    • (2011) ISRR
    • Huang, A.S.1    Bachrach, A.2    Henry, P.3    Krainin, M.4    Maturana, D.5    Fox, D.6    Roy, N.7
  • 3
    • 84906713426 scopus 로고    scopus 로고
    • The planner ensemble and trajectory executive: A high performance motion planning system with guaranteed safety
    • May
    • S. Choudhury, S. Arora, and S. Scherer, "The planner ensemble and trajectory executive: A high performance motion planning system with guaranteed safety, " in AHS, May 2014
    • (2014) AHS
    • Choudhury, S.1    Arora, S.2    Scherer, S.3
  • 4
    • 84864480641 scopus 로고    scopus 로고
    • An occlusion-aware feature for range images
    • May 14-18
    • A. Quadros, J. Underwood, and B. Douillard, "An occlusion-aware feature for range images, " in ICRA, May 14-18 2012
    • (2012) ICRA
    • Quadros, A.1    Underwood, J.2    Douillard, B.3
  • 5
    • 84886073305 scopus 로고    scopus 로고
    • Indoor segmentation and support inference from rgbd images
    • P. K. Nathan Silberman, Derek Hoiem and R. Fergus, "Indoor segmentation and support inference from rgbd images, " in ECCV, 2012
    • (2012) ECCV
    • Nathan Silberman, P.K.1    Hoiem, D.2    Fergus, R.3
  • 7
    • 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, pp. 1097-1105
    • (2012) NIPS , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 10
    • 20344381451 scopus 로고    scopus 로고
    • Recognizing objects in range data using regional point descriptors
    • A. Frome, D. Huber, and R. Kolluri, "Recognizing objects in range data using regional point descriptors, " ECCV, vol. 1, pp. 1-14, 2004
    • (2004) ECCV , vol.1 , pp. 1-14
    • Frome, A.1    Huber, D.2    Kolluri, R.3
  • 11
    • 84864421786 scopus 로고    scopus 로고
    • Performance of histogram descriptors for the classification of 3D laser range data in urban environments
    • J. Behley, V. Steinhage, and A. B. Cremers, "Performance of histogram descriptors for the classification of 3D laser range data in urban environments, " in ICRA, 2012, pp. 4391-4398
    • (2012) ICRA , pp. 4391-4398
    • Behley, J.1    Steinhage, V.2    Cremers, A.B.3
  • 12
    • 84864419094 scopus 로고    scopus 로고
    • Towards 3D object recognition via classification of arbitrary object tracks
    • A. Teichman, J. Levinson, and S. Thrun, "Towards 3D object recognition via classification of arbitrary object tracks, " in ICRA, 2011, pp. 4034-4041
    • (2011) ICRA , pp. 4034-4041
    • Teichman, A.1    Levinson, J.2    Thrun, S.3
  • 13
    • 77953198354 scopus 로고    scopus 로고
    • Shape-based recognition of 3D point clouds in urban environments
    • A. Golovinskiy, V. G. Kim, and T. Funkhouser, "Shape-based recognition of 3D point clouds in urban environments, " ICCV, 2009
    • (2009) ICCV
    • Golovinskiy, A.1    Kim, V.G.2    Funkhouser, T.3
  • 14
    • 80053350122 scopus 로고    scopus 로고
    • Onboard contextual classification of 3-D point clouds with learned high-order markov random fields
    • D. Munoz, N. Vandapel, and M. Hebert, "Onboard contextual classification of 3-D point clouds with learned high-order markov random fields, " in ICRA, 2009
    • (2009) ICRA
    • Munoz, D.1    Vandapel, N.2    Hebert, M.3
  • 15
    • 85162558717 scopus 로고    scopus 로고
    • Semantic labeling of 3D point clouds for indoor scenes
    • H. Koppula, "Semantic labeling of 3D point clouds for indoor scenes, " NIPS, 2011
    • (2011) NIPS
    • Koppula, H.1
  • 16
    • 84866653395 scopus 로고    scopus 로고
    • RGB-(D) scene labeling: Features and algorithms
    • X. Ren, L. Bo, and D. Fox, "RGB-(D) scene labeling: Features and algorithms, " in CVPR, 2012
    • (2012) CVPR
    • Ren, X.1    Bo, L.2    Fox, D.3
  • 17
    • 84893741253 scopus 로고    scopus 로고
    • Deep learning for detecting robotic grasps
    • I. Lenz, H. Lee, and A. Saxena, "Deep learning for detecting robotic grasps, " in RSS, 2013
    • (2013) RSS
    • Lenz, I.1    Lee, H.2    Saxena, A.3
  • 18
    • 84877789646 scopus 로고    scopus 로고
    • Convolutional-recursive deep learning for 3d object classification
    • Richard Socher and Brody Huval and Bharath Bhat and Christopher D. Manning and Andrew Y. Ng, "Convolutional-Recursive Deep Learning for 3D Object Classification, " in NIPS, 2012
    • (2012) NIPS
    • Socher, R.1    Huval, B.2    Bhat, B.3    Manning, C.D.4    Ng, A.Y.5
  • 19
    • 84977871979 scopus 로고    scopus 로고
    • 3D object recognition using convolutional neural networks with transfer learning between input channels
    • L. A. Alexandre, "3D object recognition using convolutional neural networks with transfer learning between input channels, " in IAS, vol. 301, 2014
    • (2014) IAS , vol.301
    • Alexandre, L.A.1
  • 20
    • 84921446587 scopus 로고    scopus 로고
    • Fast semantic segmentation of RGBD scenes with GPU-accelerated deep neural networks
    • N. Höft, H. Schulz, and S. Behnke, "Fast semantic segmentation of RGBD scenes with gpu-accelerated deep neural networks, " in 37th Annual German Conference on AI, 2014, pp. 80-85
    • (2014) 37th Annual German Conference on AI , pp. 80-85
    • Höft, N.1    Schulz, H.2    Behnke, S.3
  • 21
    • 84911498680 scopus 로고    scopus 로고
    • Unsupervised feature learning for classification of outdoor 3d scans
    • M. De Deuge, A. Quadros, C. Hung, and B. Douillard, "Unsupervised feature learning for classification of outdoor 3d scans, " in ACRA, 2013
    • (2013) ACRA
    • De Deuge, M.1    Quadros, A.2    Hung, C.3    Douillard, B.4
  • 22
    • 84922645579 scopus 로고    scopus 로고
    • Learning rich features from RGB-D images for object detection and segmentation
    • S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, "Learning rich features from RGB-D images for object detection and segmentation, " in ECCV, 2014
    • (2014) ECCV
    • Gupta, S.1    Girshick, R.2    Arbeláez, P.3    Malik, J.4
  • 23
    • 84870183903 scopus 로고    scopus 로고
    • 3D convolutional neural networks for human action recognition
    • S. Ji, W. Xu, M. Yang, and K. Yu, "3D convolutional neural networks for human action recognition, " IEEE TPAMI, vol. 35, no. 1, pp. 221-231, 2013
    • (2013) IEEE TPAMI , vol.35 , Issue.1 , pp. 221-231
    • Ji, S.1    Xu, W.2    Yang, M.3    Yu, K.4
  • 25
    • 84929146905 scopus 로고    scopus 로고
    • Unsupervised feature learning for 3D scene labeling
    • K. Lai, L. Bo, and D. Fox, "Unsupervised feature learning for 3D scene labeling, " in ICRA, 2014
    • (2014) ICRA
    • Lai, K.1    Bo, L.2    Fox, D.3
  • 26
    • 84949636429 scopus 로고    scopus 로고
    • 3d shapenets: A deep representation for volumetric shape modeling
    • Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, "3d shapenets: A deep representation for volumetric shape modeling, " in CVPR, 2015
    • (2015) CVPR
    • Wu, Z.1    Song, S.2    Khosla, A.3    Yu, F.4    Zhang, L.5    Tang, X.6    Xiao, J.7
  • 27
    • 77951939610 scopus 로고    scopus 로고
    • A convolutional learning system for object classification in 3-D lidar data
    • May
    • D. Prokhorov, "A convolutional learning system for object classification in 3-D lidar data, " IEEE TNN, vol. 21, no. 5, pp. 858-863, May 2010
    • (2010) IEEE TNN , vol.21 , Issue.5 , pp. 858-863
    • Prokhorov, D.1
  • 28
    • 84938228349 scopus 로고    scopus 로고
    • 3D convolutional neural networks for landing zone detection from lidar
    • D. Maturana and S. Scherer, "3D convolutional neural networks for landing zone detection from lidar, " in ICRA, 2015
    • (2015) ICRA
    • Maturana, D.1    Scherer, S.2
  • 29
  • 30
    • 85041522555 scopus 로고
    • High resolution maps from wide angle sonar
    • H. Moravec and A. Elfes, "High resolution maps from wide angle sonar, " in ICRA, 1985
    • (1985) ICRA
    • Moravec, H.1    Elfes, A.2
  • 31
    • 0141904789 scopus 로고    scopus 로고
    • Learning occupancy grid maps with forward sensor models
    • S. Thrun, "Learning occupancy grid maps with forward sensor models, " Auton. Robots, vol. 15, no. 2, pp. 111-127, 2003
    • (2003) Auton. Robots , vol.15 , Issue.2 , pp. 111-127
    • Thrun, S.1
  • 32
    • 0002647811 scopus 로고
    • A fast voxel traversal algorithm for ray tracing
    • Aug.
    • J. Amanatides and A. Woo, "A fast voxel traversal algorithm for ray tracing, " in Eurographics '87, Aug. 1987, pp. 3-10
    • (1987) Eurographics '87 , pp. 3-10
    • Amanatides, J.1    Woo, A.2
  • 33
    • 0036448726 scopus 로고    scopus 로고
    • Map building with mobile robots in populated environments
    • D. Hähnel, D. Schulz, and W. Burgard, "Map building with mobile robots in populated environments, " in IROS, 2002
    • (2002) IROS
    • Hähnel, D.1    Schulz, D.2    Burgard, W.3
  • 34
    • 77955795720 scopus 로고    scopus 로고
    • FLIRT-interest regions for 2D range data
    • G. D. Tipaldi and K. O. Arras, "FLIRT-interest regions for 2D range data, " in ICRA, 2010
    • (2010) ICRA
    • Tipaldi, G.D.1    Arras, K.O.2
  • 35
    • 84893676344 scopus 로고    scopus 로고
    • Rectifier nonlinearities improve neural network acoustic models
    • A. L. Maas, A. Y. Hannun, and A. Y. Ng, "Rectifier nonlinearities improve neural network acoustic models, " in ICML, vol. 30, 2013
    • (2013) ICML , vol.30
    • Maas, A.L.1    Hannun, A.Y.2    Ng, A.Y.3
  • 36


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.