Fetching the paper…
Reading the bibliography…
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.
Study for applying computer-generated images to visual simulation
R. A. Schumacker, B. Brand, M. G. Gilliland, and W. H. Sharp · 1969
Earlier work this paper cites.
Multidimensional binary search trees used for associative searching
J. L. Bentley · 1975
Earlier work this paper cites.
Constructive Solid Geometry
A. Requicha, H. Voelcker, and U. of Rochester. Production Automation Project · 1977
Earlier work this paper cites.
Octree encoding: A new technique for the representation, manipulation and display of arbitrary 3-d objects by computer
D. J. Meagher · 1980
Earlier work this paper cites.
R-trees: A Dynamic Index Structure for Spatial Searching
A. Guttman, M. Stonebraker, and C. U. B. E. R. LAB · 1983
Earlier work this paper cites.
The design and analysis of spatial data structures
H. Samet · 1990
Earlier work this paper cites.
Signature verification using a “siamese” time delay neural network
J. Bromley, J. W. Bentz, L. Bottou, I. Guyon, Y. LeCun, C. Moore, E. Säckinger, and R. Shah · 1993
Earlier work this paper cites.
Introduction to computer graphics
J. D. Foley, A. Van Dam, S. K. Feiner, J. F. Hughes, and R. L. Phillips · 1994
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Learning a distance metric from relative comparisons
M. Schultz and T. Joachims · 2004
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
Earlier work this paper cites.
Parsing natural scenes and natural language with recursive neural networks
R. Socher, C. C. Lin, C. Manning, and A. Y. Ng · 2011
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2013
Earlier work this paper cites.
Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks
D. Boscaini, J. Masci, S. Melzi, M. M. Bronstein, U. Castellani, and P. Vandergheynst · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Lasagne: First release., Aug. 2015
S. Dieleman, J. Schlüter, C. Raffel, E. Olson, et al · 2015
Cited alongside, same era.
Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Voxnet: A 3d convolutional neural network for real-time object recognition
Fusionnet: 3d object classification using multiple data representations
V. Hegde and R. Zadeh · 2016
Later among the works it cites.
TI-POOLING: transformation-invariant pooling for feature learning in convolutional neural networks
D. Laptev, N. Savinov, J. M. Buhmann, and M. Pollefeys · 2016
Later among the works it cites.
Fpnn: Field probing neural networks for 3d data
Y. Li, S. Pirk, H. Su, C. R. Qi, and L. J. Guibas · 2016
Later among the works it cites.
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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Maturana and S. Scherer · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Multi-view convolutional neural networks for 3d shape recognition
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller · 2015
Cited alongside, same era.
Voting for voting in online point cloud object detection
D. Z. Wang and I. Posner · 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.
Learning shape correspondence with anisotropic convolutional neural networks
D. Boscaini, J. Masci, E. Rodolà, and M. M. Bronstein · 2016
Cited alongside, same era.
Generative and discriminative voxel modeling with convolutional neural networks
A. Brock, T. Lim, J. Ritchie, and N. Weston · 2016
Cited alongside, same era.
SHREC’16 track large-scale 3d shape retrieval from ShapeNet Core-55
M. Savva, F. Yu, H. Su, M. Aono, B. Chen, D. Cohen-Or, W. Deng, H. Su, S. Bai, X. Bai, et al · 2016
Later among the works it cites.
Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
Later among the works it cites.
Learning deep embeddings with histogram loss
E. Ustinova and V. S. Lempitsky · 2016
Later among the works it cites.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
Later among the works it cites.
A scalable active framework for region annotation in 3d shape collections
L. Yi, V. G. Kim, D. Ceylan, I. Shen, M. Yan, H. Su, A. Lu, Q. Huang, A. Sheffer, L. Guibas, et al · 2016
Later among the works it cites.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
Closest in time.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
M. Simonovsky and N. Komodakis · 2017
Closest in time.