Fetching the paper…
Reading the bibliography…
Object detection through LiDAR-based point cloud has recently been important in autonomous driving.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3d shapenets: A deep representation for volumetric shapes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1912–1920
1920
Earlier work this paper cites.
T. W. Sederberg and S. R. Parry, “Free-form deformation of solid geometric models,” in Proceedings of the 13th annual conference on Computer graphics and interactive techniques , 1986, pp. 151–160
1986
Earlier work this paper cites.
B. Chen and A. Kaufman, “3d volume rotation using shear transformations,” Graphical Models , vol. 62, no. 4, pp. 308–322, 2000
2000
Earlier work this paper cites.
D. A. Van Dyk and X.-L. Meng, “The art of data augmentation,” Journal of Computational and Graphical Statistics , vol. 10, no. 1, pp. 1–50, 2001
2001
Earlier work this paper cites.
A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola, “A kernel method for the two-sample-problem,” Advances in neural information processing systems , vol. 19, 2006
2006
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
R. H. Rasshofer, M. Spies, and H. Spies, “Influences of weather phenomena on automotive laser radar systems,” Advances in radio science , vol. 9, no. B. 2, pp. 49–60, 2011
2011
Earlier work this paper cites.
H. Ma and J. Wu, “Analysis of positioning errors caused by platform vibration of airborne lidar system,” in 2012 8th IEEE International Symposium on Instrumentation and Control Technology (ISICT) Proceedings . IEEE, 2012, pp. 257–261
2012
Earlier work this paper cites.
L. Mona, Z. Liu, D. Müller, A. Omar, A. Papayannis, G. Pappalardo, N. Sugimoto, and M. Vaughan, “Lidar measurements for desert dust characterization: an overview,” Advances in Meteorology , vol. 2012, 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
T. Instruments, “Lidar pulsed time of flight reference design,” 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
D. Bolkas and A. Martinez, “Effect of target color and scanning geometry on terrestrial lidar point-cloud noise and plane fitting,” Journal of applied geodesy , vol. 12, no. 1, pp. 109–127, 2018
2018
Earlier work this paper cites.
B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d object detection from point clouds,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 7652–7660
2018
Earlier work this paper cites.
Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutional detection,” Sensors , vol. 18, no. 10, p. 3337, 2018
2018
Earlier work this paper cites.
X. Ning, F. Li, G. Tian, and Y. Wang, “An efficient outlier removal method for scattered point cloud data,” PloS one , vol. 13, no. 8, p. e0201280, 2018
2018
Earlier work this paper cites.
A. Carrilho, M. Galo, and R. Santos, “Statistical outlier detection method for airborne lidar data.” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences , 2018
2018
Earlier work this paper cites.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A survey on 3d object detection methods for autonomous driving applications,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 10, pp. 3782–3795, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz, “Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
M.-F. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan et al. , “Argoverse: 3d tracking and forecasting with rich maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8748–8757
2019
Cited alongside, same era.
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet, “Level 5 perception dataset 2020,” https://level-5.global/level5/data/ , 2019
2019
Cited alongside, same era.
C. Xiang, C. R. Qi, and B. Li, “Generating 3d adversarial point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9136–9144
2019
Cited alongside, same era.
D. Liu, R. Yu, and H. Su, “Extending adversarial attacks and defenses to deep 3d point cloud classifiers,” in 2019 IEEE International Conference on Image Processing (ICIP) . IEEE, 2019, pp. 2279–2283
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Pitropov, D. E. Garcia, J. Rebello, M. Smart, C. Wang, K. Czarnecki, and S. Waslander, “Canadian adverse driving conditions dataset,” The International Journal of Robotics Research , vol. 40, no. 4-5, pp. 681–690, 2021
2021
Later among the works it cites.
F. Villa, F. Severini, F. Madonini, and F. Zappa, “Spads and sipms arrays for long-range high-speed light detection and ranging (lidar),” Sensors , vol. 21, no. 11, p. 3839, 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2019
Cited alongside, same era.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 770–779
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep learning for 3d point clouds: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 12, pp. 4338–4364, 2020
2020
Cited alongside, same era.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Cited alongside, same era.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2446–2454
2020
Cited alongside, same era.
M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer, and F. Heide, “Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Cited alongside, same era.
C.-C. Wong and C.-M. Vong, “Efficient outdoor 3d point cloud semantic segmentation for critical road objects and distributed contexts,” in European Conference on Computer Vision . Springer, 2020, pp. 499–514
2020
Cited alongside, same era.
Y. Wang, X. Chen, Y. You, L. E. Li, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L. Chao, “Train in germany, test in the usa: Making 3d object detectors generalize,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 713–11 723
2020
Cited alongside, same era.
R. Wang, B. Wang, M. Xiang, C. Li, S. Wang, and C. Song, “Simultaneous time-varying vibration and nonlinearity compensation for one-period triangular-fmcw lidar signal,” Remote Sensing , vol. 13, no. 9, p. 1731, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Abdelfattah, K. Yuan, Z. J. Wang, and R. Ward, “Adversarial attacks on camera-lidar models for 3d car detection,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 2189–2194
2021
Later among the works it cites.
Y. Zhu, C. Miao, F. Hajiaghajani, M. Huai, L. Su, and C. Qiao, “Adversarial attacks against lidar semantic segmentation in autonomous driving,” in Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems , 2021, pp. 329–342
2021
Later among the works it cites.
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu, and C. Xu, “Voxel transformer for 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 3164–3173
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Lee, J. Lee, J. Lee, H. Lee, M. Lee, S. Woo, and S. Lee, “Regularization strategy for point cloud via rigidly mixed sample,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 900–15 909
2021
Later among the works it cites.
J. Fang, X. Zuo, D. Zhou, S. Jin, S. Wang, and L. Zhang, “Lidar-aug: A general rendering-based augmentation framework for 3d object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4710–4720
2021
Later among the works it cites.
M. Hahner, C. Sakaridis, D. Dai, and L. Van Gool, “Fog simulation on real lidar point clouds for 3d object detection in adverse weather,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 283–15 292
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Zhang, R. Girdhar, A. Joulin, and I. Misra, “Self-supervised pretraining of 3d features on any point-cloud,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 252–10 263
2021
Later among the works it cites.
Y. Duan, C. Yang, H. Chen, W. Yan, and H. Li, “Low-complexity point cloud denoising for lidar by pca-based dimension reduction,” Optics Communications , vol. 482, p. 126567, 2021
2021
Later among the works it cites.
T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 11 784–11 793
2021
Later among the works it cites.
W. Zheng, W. Tang, L. Jiang, and C.-W. Fu, “Se-ssd: Self-ensembling single-stage object detector from point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 494–14 503
2021
Later among the works it cites.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Wikipedia contributors, “Rain — Wikipedia, the free encyclopedia,” https://en.wikipedia.org/w/index.php?title=Rain&oldid=1102188829 , 2022, [Online; accessed 5-August-2022]
2022
Closest in time.