Fetching the paper…
Reading the bibliography…
Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid.
Chetlur, S., Woolley, C., Vandermersch, P., Cohen, J., Tran, J., Catanzaro, B., Shelhamer, E.: cudnn: Efficient primitives for deep learning. CoRR (2014)
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Jaderberg, M., Simonyan, K., Zisserman, A., kavukcuoglu, k.: Spatial transformer networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NIPS), pp. 2017–2025. Curran Associates, Inc. (2015)
2015
Earlier work this paper cites.
Maturana, D., Scherer, S.: VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition. In: International Conference on Intelligent Robots and Systems (2015)
2015
Earlier work this paper cites.
Ronneberger, O., P.Fischer, Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). LNCS, vol. 9351, pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1912–1920 (2015)
2015
Earlier work this paper cites.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
2015
Earlier work this paper cites.
Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
De Brabandere, B., Jia, X., Tuytelaars, T., Van Gool, L.: Dynamic filter networks. In: Advances in Neural Information Processing Systems (NIPS) (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 630–645 (2016)
2016
Cited alongside, same era.
Lavin, A., Gray, S.: Fast algorithms for convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4013–4021 (2016)
2016
Cited alongside, same era.
Qi, C.R., Su, H., Niessner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view cnns for object classification on 3d data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NIPS), pp. 5099–5108. Curran Associates, Inc. (2017)
2017
Later among the works it cites.
Riegler, G., Ulusoy, A.O., Bischof, H., Geiger, A.: Octnetfusion: Learning depth fusion from data. In: International Conference on 3D Vision (3DV) (Oct 2017)
2017
Later among the works it cites.
Sfikas, K., Pratikakis, I., Theoharis, T.: Ensemble of panorama-based convolutional neural networks for 3d model classification and retrieval. Computers and Graphics (2017)
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Armeni, I., Sax, A., Zamir, A.R., Savarese, S.: Joint 2D-3D-Semantic Data for Indoor Scene Understanding. ArXiv e-prints (Feb 2017)
2017
Cited alongside, same era.
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (2017)
2017
Cited alongside, same era.
Cao, Z., Huang, Q., Karthik, R.: 3d object classification via spherical projections. In: International Conference on 3D Vision (3DV). pp. 566–574. IEEE (2017)
2017
Cited alongside, same era.
Groh, F., Resch, B., Lensch, H.P.A.: Multi-view continuous structured light scanning. In: Pattern Recognition - 39th German Conference, GCPR 2017, Basel, Switzerland, September 12-15, 2017, Proceedings. pp. 377–388 (2017). https://doi.org/10.1007/978-3-319-66709-6_30
2017
Cited alongside, same era.
Hershey, S., Chaudhuri, S., Ellis, D.P.W., Gemmeke, J.F., Jansen, A., Moore, C., Plakal, M., Platt, D., Saurous, R.A., Seybold, B., Slaney, M., Weiss, R., Wilson, K.: Cnn architectures for large-scale audio classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2017)
2017
Cited alongside, same era.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)
2017
Cited alongside, same era.
Klokov, R., Lempitsky, V.: Escape from cells: Deep kd-networks for the recognition of 3d point cloud models. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). pp. 863–872 (10 2017)
2017
Cited alongside, same era.
Later among the works it cites.
2017
Later among the works it cites.
Wieschollek, P., Schölkopf, M.H.B., Lensch, H.P.A.: Learning blind motion deblurring. In: International Conference on Computer Vision (ICCV) (October 2017)
2017
Later among the works it cites.
2018
Closest in time.
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: SPLATNet: Sparse lattice networks for point cloud processing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2530–2539 (2018)
2018
Closest in time.
Vasilache, N., Zinenko, O., Theodoridis, T., Goyal, P., DeVito, Z., Moses, W.S., Verdoolaege, S., Adams, A., Cohen, A.: Tensor comprehensions: Framework-agnostic high-performance machine learning abstractions (2018)
2018
Closest in time.
Wang, W., Yu, R., Huang, Q., Neumann, U.: Sgpn: Similarity group proposal network for 3d point cloud instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2569–2578 (2018)
2018
Closest in time.