Fetching the paper…
Reading the bibliography…
In this paper, we introduce a new method for classifying 3D objects.
Shape distributions
R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin · 2002
Earlier work this paper cites.
Rotation invariant spherical harmonic representation of 3d shape descriptors
M. Kazhdan, T. Funkhouser, and S. Rusinkiewicz · 2003
Earlier work this paper cites.
Three-dimensional shape searching: State-of-the-art review and future trends
N. Iyer, S. Jayanti, K. Lou, Y. Kalyanaraman, and K. Ramani · 2005
Earlier work this paper cites.
On visual similarity based 2d drawing retrieval
J. Pu and K. Ramani · 2006
Earlier work this paper cites.
Mesh parameterization: Theory and practice
K. Hormann, K. Polthier, and A. Sheffer · 2008
Earlier work this paper cites.
Hough transform and 3d surf for robust three dimensional classification
J. Knopp, M. Prasad, G. Willems, R. Timofte, and L. Van Gool · 2010
Earlier work this paper cites.
Fine-grained semi-supervised labeling of large shape collections
Q.-X. Huang, H. Su, and L. Guibas · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q.-X. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
Cited alongside, same era.
Deeppano: Deep panoramic representation for 3-d shape recognition
B. Shi, S. Bai, Z. Zhou, and X. Bai · 2015
Cited alongside, same era.
Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller · 2015
Cited alongside, same era.
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.
Orientation-boosted voxel nets for 3d object recognition
N. S. Alvar, M. Zolfaghari, and T. Brox · 2016
Cited alongside, same era.
Generative and discriminative voxel modeling with convolutional neural networks
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2016
Later among the works it cites.
Volumetric and multi-view cnns for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 2016
Later among the works it cites.
Deep learning with sets and point clouds
S. Ravanbakhsh, J. G. Schneider, and B. Póczos · 2016
Later among the works it cites.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2016
Later among the works it cites.
Deep learning 3d shape surfaces using geometry images
A. Sinha, J. Bai, and K. Ramani · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Brock, T. Lim, J. Ritchie, and N. Weston · 2016
Cited alongside, same era.
Pointnet: A 3d convolutional neural network for real-time object class recognition
A. Garcia-Garcia, F. Gomez-Donoso, J. G. Rodríguez, S. Orts-Escolano, M. Cazorla, and J. A. López · 2016
Cited alongside, same era.
Fusionnet: 3d object classification using multiple data representations
V. Hegde and R. Zadeh · 2016
Cited alongside, same era.
Pairwise decomposition of image sequences for active multi-view recognition
E. Johns, S. Leutenegger, and A. J. Davison · 2016
Cited alongside, same era.
Beam search for learning a deep convolutional neural network of 3d shapes
X. Xu and S. Todorovic · 2016
Later among the works it cites.
Octnetfusion: Learning depth fusion from data
G. Riegler, A. O. Ulusoy, H. Bischof, and A. Geiger · 2017
Closest in time.
Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval
K. Sfikas, T. Theoharis, and I. Pratikakis · 2017
Closest in time.
Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
Closest in time.