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We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
Imagenet classification with deep convolutional neural networks
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Indoor segmentation and support inference from rgbd images
P. K. Nathan Silberman, Derek Hoiem and R. Fergus · 2012
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 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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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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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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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Generating sentences from a continuous space
S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio · 2015
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al · 2015
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Fast r-cnn
R. Girshick · 2015
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Deformable part models are convolutional neural networks
R. Girshick, F. Iandola, T. Darrell, and J. Malik · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 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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Learning structured output representation using deep conditional generative models
K. Sohn, H. Lee, and X. Yan · 2015
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Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 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
Cited alongside, same era.
Instance-sensitive fully convolutional networks
J. Dai, K. He, Y. Li, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Pixelvae: A latent variable model for natural images
I. Gulrajani, K. Kumar, F. Ahmed, A. A. Taiga, F. Visin, D. Vazquez, and A. Courville · 2016
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Fully convolutional instance-aware semantic segmentation
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei · 2016
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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Learning to refine object segments
3d bounding box estimation using deep learning and geometry
A. Mousavian, D. Anguelov, J. Flynn, and J. Košecká · 2017
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The shape variational autoencoder: A deep generative model of part-segmented 3d objects
C. Nash and C. K. Williams · 2017
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Frustum pointnets for 3d object detection from rgb-d data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Deep sliding shapes for amodal 3d object detection in rgb-d images
S. Song and J. Xiao · 2016
Cited alongside, same era.
A scalable active framework for region annotation in 3d shape collections
L. Yi, V. G. Kim, D. Ceylan, I. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, L. Guibas, et al · 2016
Cited alongside, same era.
Multi-view 3d object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
Cited alongside, same era.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. A. Funkhouser, and M. Nießner · 2017
Cited alongside, same era.
Amodal detection of 3d objects: Inferring 3d bounding boxes from 2d ones in rgb-depth images
Z. Deng and L. J. Latecki · 2017
Cited alongside, same era.
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
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Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
L. Yi, H. Su, X. Guo, and L. J. Guibas · 2017
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2017
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http://kaldir.vc.in.tum.de/scannet_benchmark , Accessed 2018-12-7, 5pm
Scannet 3d semantic instance benchmark leader board · 2018
Closest in time.
Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2018
Closest in time.
Path aggregation network for instance segmentation
S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia · 2018
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K. Mo, S. Zhu, A. Chang, L. Yi, S. Tripathi, L. Guibas, and H. Su · 2018
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Sgpn: Similarity group proposal network for 3d point cloud instance segmentation
W. Wang, R. Yu, Q. Huang, and U. Neumann · 2018
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Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes
Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox · 2018
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Pixor: Real-time 3d object detection from point clouds
B. Yang, W. Luo, and R. Urtasun · 2018
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