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We focus on the task of amodal 3D object detection in RGB-D images, which aims to produce a 3D bounding box of an object in metric form at its full extent.
Indoor segmentation and support inference from RGBD images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 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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Unsupervised feature learning for RGB-D based object recognition
L. Bo, X. Ren, and D. Fox · 2013
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Perceptual organization and recognition of indoor scenes from RGB-D images
S. Gupta, P. Arbelaez, and J. Malik · 2013
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Selective search for object recognition
J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders · 2013
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Robust real-time visual odometry for dense rgb-d mapping
T. Whelan, H. Johannsson, M. Kaess, J. J. Leonard, and J. McDonald · 2013
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Multiscale combinatorial grouping
P. Arbelaez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik · 2014
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Learning hierarchical sparse features for rgb-(d) object recognition
L. Bo, X. Ren, and D. Fox · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Learning rich features from RGB-D images for object detection and segmentation
S. Gupta, R. Girshick, P. Arbelaez, and J. Malik · 2014
Earlier work this paper cites.
Unsupervised feature learning for 3d scene labeling
K. Lai, L. Bo, and D. Fox · 2014
Cited alongside, same era.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Sliding Shapes for 3D object detection in depth images
S. Song and J. Xiao · 2014
Cited alongside, same era.
3d object proposals for accurate object class detection
X. Chen, K. Kunku, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler, and R. Urtasun · 2015
Cited alongside, same era.
3D deep shape descriptor
Y. Fang, J. Xie, G. Dai, M. Wang, F. Zhu, T. Xu, and E. Wong · 2015
Cited alongside, same era.
Amodal completion and size constancy in natural scenes
A. Kar, S. Tulsiani, J. Carreira, and J. Malik · 2015
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R-CNN minus R
K. Lenc and A. Vedaldi · 2015
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VoxNet: A 3D convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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DeepPano: Deep panoramic representation for 3-D shape recognition
B. Shi, S. Bai, Z. Zhou, and X. Bai · 2015
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Multi-view convolutional neural networks for 3D shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller · 2015
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Fast R-CNN
R. Girshick · 2015
Cited alongside, same era.
Aligning 3D models to RGB-D images of cluttered scenes
S. Gupta, P. A. Arbeláez, R. B. Girshick, and J. Malik · 2015
Cited alongside, same era.
Cross modal distillation for supervision transfer
S. Gupta, J. Hoffman, and J. Malik · 2015
Cited alongside, same era.
Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces
H. Huang, E. Kalogerakis, and B. Marlin · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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DeepShape: Deep learned shape descriptor for 3D shape matching and retrieval
J. Xie, Y. Fang, F. Zhu, and E. Wong · 2015
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Three-dimensional object detection and layout prediction using clouds of oriented gradients
R. Zhile and E. B. Sudderth · 2016
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