Fetching the paper…
Reading the bibliography…
We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry.
Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points, vol. 26.2. ACM (1992)
1992
Earlier work this paper cites.
Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on pattern analysis and machine intelligence 21
1999
Earlier work this paper cites.
Lowe, D.G.: Object recognition from local scale-invariant features. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on. vol. 2, pp. 1150–1157. Ieee (1999)
1999
Earlier work this paper cites.
Maaten, L.v.d., Hinton, G.: Visualizing data using t-sne. Journal of machine learning research 9
2008
Earlier work this paper cites.
Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (fpfh) for 3d registration. In: Robotics and Automation, 2009. ICRA’09. IEEE International Conference on. pp. 3212–3217. IEEE (2009)
2009
Earlier work this paper cites.
Tombari, F., Salti, S., Di Stefano, L.: Unique shape context for 3d data description. In: Proceedings of the ACM workshop on 3D object retrieval. pp. 57–62. ACM (2010)
2010
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)
2012
Earlier work this paper cites.
Guo, Y., Sohel, F.A., Bennamoun, M., Wan, J., Lu, M.: Rops: A local feature descriptor for 3d rigid objects based on rotational projection statistics. In: Communications, Signal Processing, and their Applications (ICCSPA), 2013 1st International Conference on. pp. 1–6. IEEE (2013)
2013
Earlier work this paper cites.
Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.: Scene coordinate regression forests for camera relocalization in rgb-d images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2930–2937 (2013)
2013
Earlier work this paper cites.
van der Maaten, L.: Barnes-Hut-SNE. ArXiv e-prints (Jan 2013)
2013
Earlier work this paper cites.
Xiao, J., Owens, A., Torralba, A.: Sun3d: A database of big spaces reconstructed using sfm and object labels. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1625–1632 (2013)
2013
Earlier work this paper cites.
Lai, K., Bo, L., Fox, D.: Unsupervised feature learning for 3d scene labeling. In: Robotics and Automation (ICRA), 2014 IEEE International Conference on. pp. 3050–3057. IEEE (2014)
2014
Earlier work this paper cites.
Salti, S., Tombari, F., Di Stefano, L.: Shot: Unique signatures of histograms for surface and texture description. Computer Vision and Image Understanding 125
2014
Earlier work this paper cites.
Birdal, T., Ilic, S.: Point pair features based object detection and pose estimation revisited. In: 3D Vision. pp. 527–535. IEEE (2015)
2015
Earlier work this paper cites.
Kinga, D., Adam, J.B.: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
2015
Earlier work this paper cites.
Maturana, D., Scherer, S.: Voxnet: A 3d convolutional neural network for real-time object recognition. In: Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on. pp. 922–928. IEEE (2015)
2015
Earlier work this paper cites.
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Transactions on Robotics 31
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. pp. 1912–1920 (2015)
2015
Cited alongside, same era.
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Cited alongside, same era.
Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation. In: European Conference on Computer Vision. pp. 205–220. Springer (2016)
2016
Cited alongside, same era.
Noh, H., Araujo, A., Sim, J., Weyand, T., Han, B.: Large-scale image retrieval with attentive deep local features. In: The IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
Later among the works it cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE 1
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 30, pp. 5099–5108. Curran Associates, Inc. (2017)
2017
Later among the works it cites.
Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R.: 3d graph neural networks for rgbd semantic segmentation. In: The IEEE International Conference on Computer Vision (ICCV) (Oct 2017)
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Valentin, J., Dai, A., Nießner, M., Kohli, P., Torr, P., Izadi, S., Keskin, C.: Learning to navigate the energy landscape. In: 3D Vision (3DV), 2016 Fourth International Conference on. pp. 323–332. IEEE (2016)
2016
Cited alongside, same era.
Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling. In: Advances in Neural Information Processing Systems. pp. 82–90 (2016)
2016
Cited alongside, same era.
Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: Lift: Learned invariant feature transform. In: European Conference on Computer Vision. pp. 467–483. Springer (2016)
2016
Cited alongside, same era.
Birdal, T., Ilic, S.: Cad priors for accurate and flexible instance reconstruction. In: Computer Vision (ICCV), 2017 IEEE International Conference on. pp. 133–142. IEEE (2017)
2017
Cited alongside, same era.
Birdal, T., Ilic, S.: A point sampling algorithm for 3d matching of irregular geometries. In: International Conference on Intelligent Robots and Systems (IROS 2017). IEEE (2017)
2017
Cited alongside, same era.
Cao, Z., Huang, Q., Karthik, R.: 3d object classification via spherical projections. In: 3D Vision (3DV), 2017 International Conference on. pp. 566–574. IEEE (2017)
2017
Cited alongside, same era.
Elbaz, G., Avraham, T., Fischer, A.: 3d point cloud registration for localization using a deep neural network auto-encoder. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (July 2017)
2017
Cited alongside, same era.
Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., Pollefeys, M.: SEMANTIC3D.NET: A new large-scale point cloud classification benchmark. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. vol. IV-1-W1, pp. 91–98 (2017)
2017
Cited alongside, same era.
Later among the works it cites.
Riegler, G., Ulusoy, O., Geiger, A.: Octnet: Learning deep 3d representations at high resolutions. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (Jul 2017)
2017
Later among the works it cites.
Shen, Y., Feng, C., Yang, Y., Tian, D.: Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds. ArXiv e-prints (Dec 2017)
2017
Later among the works it cites.
Tatarchenko, M., Dosovitskiy, A., Brox, T.: Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs. In: IEEE International Conference on Computer Vision (ICCV) (2017)
2017
Later among the works it cites.
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-cnn: Octree-based convolutional neural networks for 3d shape analysis. ACM Transactions on Graphics (TOG) 36
2017
Later among the works it cites.
Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. In: CVPR (2017)
2017
Later among the works it cites.
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning Representations and Generative Models for 3D Point Clouds. In: International Conference on Machine Learning (ICML) (2018)
2018
Closest in time.
Bobkov, D., Chen, S., Jian, R., Iqbal, M.Z., Steinbach, E.: Noise-resistant deep learning for object classification in three-dimensional point clouds using a point pair descriptor. IEEE Robotics and Automation Letters 3
2018
Closest in time.
Deng, H., Birdal, T., Ilic, S.: Ppfnet: Global context aware local features for robust 3d point matching. Computer Vision and Pattern Recognition (CVPR). IEEE 1
2018
Closest in time.
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3d object detection from rgb-d data. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Closest in time.
Tatarchenko, M., Park, J., Koltun, V., Zhou, Q.Y.: Tangent convolutions for dense prediction in 3d. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
Closest in time.
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic Graph CNN for Learning on Point Clouds. ArXiv e-prints (Jan 2018)
2018
Closest in time.
Yang, Y., Feng, C., Shen, Y., Tian, D.: Foldingnet: Point cloud auto-encoder via deep grid deformation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Closest in time.
Yu, L., Li, X., Fu, C.W., Cohen-Or, D., Heng, P.A.: Pu-net: Point cloud upsampling network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2018)
2018
Closest in time.