Self-supervised Point Set Local Descriptors for Point Cloud Registration
- Published: 11 Mar 2020
- ShanghaiTech University China (People's Republic of)
[1] H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,” IEEE robotics & automation magazine, vol. 13, no. 2, pp. 99-110, 2006.
[2] Y. Zhong, “A shape descriptor for 3d object recognition,” in Proceedings ICCV 2009 Workshop 3DRR, vol. 6, 2009.
[3] R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (fpfh) for 3d registration,” in 2009 IEEE international conference on robotics and automation. IEEE, 2009, pp. 3212-3217.
[4] A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser, “3dmatch: Learning local geometric descriptors from rgb-d reconstructions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1802-1811.
[5] Z. Gojcic, C. Zhou, J. D. Wegner, and A. Wieser, “The perfect match: 3d point cloud matching with smoothed densities,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 5545-5554. [OpenAIRE]
[6] H. Deng, T. Birdal, and S. Ilic, “Ppfnet: Global context aware local features for robust 3d point matching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 195-205.
[7] Z. J. Yew and G. H. Lee, “3dfeat-net: Weakly supervised local 3d features for point cloud registration,” in European Conference on Computer Vision. Springer, 2018, pp. 630-646.
[8] A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with exemplar convolutional neural networks,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 9, pp. 1734-1747, 2015.
[9] S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” in International Conference on Learning Representations, 2018. [Online]. Available: https: //openreview.net/forum?id=S1v4N2l0-
[10] C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1422-1430. [OpenAIRE]
[11] R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision. Springer, 2016, pp. 649-666.
[12] J. Donahue, P. Kra¨henbu¨ hl, and T. Darrell, “Adversarial feature learning,” arXiv preprint arXiv:1605.09782, 2016.
[13] X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2794-2802.
[14] C. Vondrick, A. Shrivastava, A. Fathi, S. Guadarrama, and K. Murphy, “Tracking emerges by colorizing videos,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 391- 408. [OpenAIRE]
[15] E. Jang, C. Devin, V. Vanhoucke, and S. Levine, “Grasp2vec: Learning object representations from self-supervised grasping,” Conference on Robot Learning, 2018.
Related research
- ShanghaiTech University China (People's Republic of)
[1] H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,” IEEE robotics & automation magazine, vol. 13, no. 2, pp. 99-110, 2006.
[2] Y. Zhong, “A shape descriptor for 3d object recognition,” in Proceedings ICCV 2009 Workshop 3DRR, vol. 6, 2009.
[3] R. B. Rusu, N. Blodow, and M. Beetz, “Fast point feature histograms (fpfh) for 3d registration,” in 2009 IEEE international conference on robotics and automation. IEEE, 2009, pp. 3212-3217.
[4] A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser, “3dmatch: Learning local geometric descriptors from rgb-d reconstructions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1802-1811.
[5] Z. Gojcic, C. Zhou, J. D. Wegner, and A. Wieser, “The perfect match: 3d point cloud matching with smoothed densities,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 5545-5554. [OpenAIRE]
[6] H. Deng, T. Birdal, and S. Ilic, “Ppfnet: Global context aware local features for robust 3d point matching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 195-205.
[7] Z. J. Yew and G. H. Lee, “3dfeat-net: Weakly supervised local 3d features for point cloud registration,” in European Conference on Computer Vision. Springer, 2018, pp. 630-646.
[8] A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox, “Discriminative unsupervised feature learning with exemplar convolutional neural networks,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 9, pp. 1734-1747, 2015.
[9] S. Gidaris, P. Singh, and N. Komodakis, “Unsupervised representation learning by predicting image rotations,” in International Conference on Learning Representations, 2018. [Online]. Available: https: //openreview.net/forum?id=S1v4N2l0-
[10] C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1422-1430. [OpenAIRE]
[11] R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision. Springer, 2016, pp. 649-666.
[12] J. Donahue, P. Kra¨henbu¨ hl, and T. Darrell, “Adversarial feature learning,” arXiv preprint arXiv:1605.09782, 2016.
[13] X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2794-2802.
[14] C. Vondrick, A. Shrivastava, A. Fathi, S. Guadarrama, and K. Murphy, “Tracking emerges by colorizing videos,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 391- 408. [OpenAIRE]
[15] E. Jang, C. Devin, V. Vanhoucke, and S. Levine, “Grasp2vec: Learning object representations from self-supervised grasping,” Conference on Robot Learning, 2018.
Related research
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