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3D shape is a crucial but heavily underutilized cue in today's computer vision systems, mostly due to the lack of a good generic shape representation.
Recognition-by-components: a theory of human image understanding
I. Biederman · 1987
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Method for registration of 3-d shapes
P. J. Besl and N. D. McKay · 1992
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Active object recognition: Looking for differences
F. G. Callari and F. P. Ferrie · 2001
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Information theoretic sensor data selection for active object recognition and state estimation
J. Denzler and C. M. Brown · 2002
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Training products of experts by minimizing contrastive divergence
G. E. Hinton · 2002
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On visual similarity based 3d model retrieval
D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung · 2003
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Rotation invariant spherical harmonic representation of 3d shape descriptors
M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz · 2003
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View planning for automated 3d object reconstruction inspection
W. Scott, G. Roth, and J.-F. Rivest · 2003
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Modeling by example
T. Funkhouser, M. Kazhdan, P. Shilane, P. Min, W. Kiefer, A. Tal, S. Rusinkiewicz, and D. Dobkin · 2004
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The princeton shape benchmark
P. Shilane, P. Min, M. Kazhdan, and T. Funkhouser · 2004
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A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y.-W. Teh · 2006
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Object recognition in the geometric era: A retrospective
J. L. Mundy · 2006
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3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints
F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce · 2006
Cited alongside, same era.
Sparse deep belief net model for visual area v2
H. Lee, C. Ekanadham, and A. Y. Ng · 2007
Cited alongside, same era.
Active view selection for object and pose recognition
Z. Jia, Y.-J. Chang, and T. Chen · 2009
Cited alongside, same era.
Using fast weights to improve persistent contrastive divergence
T. Tieleman and G. Hinton · 2009
Cited alongside, same era.
A lightweight approach to repairing digitized polygon meshes
M. Attene · 2010
Cited alongside, same era.
The shape boltzmann machine: a strong model of object shape
S. M. A. Eslami, N. Heess, and J. Winn · 2012
Later among the works it cites.
A probabilistic model for component-based shape synthesis
E. Kalogerakis, S. Chaudhuri, D. Koller, and V. Koltun · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Later among the works it cites.
Indoor segmentation and support inference from rgbd images
P. K. Nathan Silberman, Derek Hoiem and R. Fergus · 2012
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Structure recovery by part assembly
C.-H. Shen, H. Fu, K. Chen, and S.-M. Hu · 2012
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Convolutional-recursive deep learning for 3d object classification
R. Socher, B. Huval, B. Bhat, C. D. Manning, and A. Y. Ng · 2012
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P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
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Cone carving for surface reconstruction
S. Shalom, A. Shamir, H. Zhang, and D. Cohen-Or · 2010
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Probabilistic reasoning for assembly-based 3d modeling
S. Chaudhuri, E. Kalogerakis, L. Guibas, and V. Koltun · 2011
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Unsupervised learning of hierarchical representations with convolutional deep belief networks
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2011
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A textured object recognition pipeline for color and depth image data
J. Tang, S. Miller, A. Singh, and P. Abbeel · 2012
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Hypothesis testing framework for active object detection
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Nonmyopic view planning for active object detection
N. Atanasov, B. Sankaran, J. L. Ny, G. J. Pappas, and K. Daniilidis · 2013
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Learning rich features from rgb-d images for object detection and segmentation
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik · 2014
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Sliding Shapes for 3D object detection in RGB-D images
S. Song and J. Xiao · 2014
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