Fetching the paper…
Reading the bibliography…
Accurate and reliable spatial and motion information plays a pivotal role in autonomous driving systems.
H. W. Kuhn, “The hungarian method for the assignment problem,” Naval research logistics quarterly , vol. 2, no. 1-2, pp. 83–97, 1955
1955
Earlier work this paper cites.
J. Carreira, R. Caseiro, J. Batista, and C. Sminchisescu, “Semantic segmentation with second-order pooling,” in European conference on computer vision . Springer, 2012, pp. 430–443
2012
Earlier work this paper cites.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Simultaneous detection and segmentation,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VII 13 . Springer, 2014, pp. 297–312
2014
Earlier work this paper cites.
D. Kinga, J. B. Adam, et al. , “A method for stochastic optimization,” in International conference on learning representations (ICLR) , vol. 5, no. 6. San Diego, California;, 2015
2015
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
O. Sener and V. Koltun, “Multi-task learning as multi-objective optimization,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
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
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
X. Liu, C. R. Qi, and L. J. Guibas, “Flownet3d: Learning scene flow in 3d point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 529–537
2019
Earlier work this paper cites.
X. Gu, Y. Wang, C. Wu, Y. J. Lee, and P. Wang, “Hplflownet: Hierarchical permutohedral lattice flownet for scene flow estimation on large-scale point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3254–3263
2019
Earlier work this paper cites.
P. Wu, S. Chen, and D. N. Metaxas, “Motionnet: Joint perception and motion prediction for autonomous driving based on bird’s eye view maps,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 385–11 395
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.
2020
Cited alongside, same era.
B. Cheng, A. Schwing, and A. Kirillov, “Per-pixel classification is not all you need for semantic segmentation,” Advances in neural information processing systems , vol. 34, pp. 17 864–17 875, 2021
W. Xiao, F. Min, H. Li, and W. Shi, “Complex motion behavior and synchronization analysis of heterogeneous neural network,” IEEE Transactions on Circuits and Systems I: Regular Papers , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Shi, K. Jiang, K. Wang, J. Li, Y. Wang, M. Yang, and D. Yang, “Streamingflow: Streaming occupancy forecasting with asynchronous multi-modal data streams via neural ordinary differential equation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 14 833–14 842
2024
Later among the works it cites.
N. Radhakrishnan, S. Kandeepan, X. Yu, and G. Baldini, “Multi step temporal spectrum occupancy prediction using deep learning,” in 2024 17th International Conference on Signal Processing and Communication System (ICSPCS) . IEEE, 2024, pp. 1–6
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
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
Cited alongside, same era.
C. Luo, X. Yang, and A. Yuille, “Self-supervised pillar motion learning for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3183–3192
2021
Cited alongside, same era.
Y. Wang, H. Pan, J. Zhu, Y.-H. Wu, X. Zhan, K. Jiang, and D. Yang, “Be-sti: Spatial-temporal integrated network for class-agnostic motion prediction with bidirectional enhancement,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 093–17 102
2022
Cited alongside, same era.
Z. Wei, X. Qi, Z. Bai, G. Wu, S. Nayak, P. Hao, M. Barth, Y. Liu, and K. Oguchi, “Spatiotemporal transformer attention network for 3d voxel level joint segmentation and motion prediction in point cloud,” in 2022 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2022, pp. 1381–1386
2022
Cited alongside, same era.
M. Toyungyernsub, E. Yel, J. Li, and M. J. Kochenderfer, “Dynamics-aware spatiotemporal occupancy prediction in urban environments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 10 836–10 841
2022
Cited alongside, same era.
R. Li, H. Shi, Z. Fu, Z. Wang, and G. Lin, “Weakly supervised class-agnostic motion prediction for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 599–17 608
2023
Cited alongside, same era.
Y. Shi, J. Li, K. Jiang, K. Wang, Y. Wang, M. Yang, and D. Yang, “Panossc: Exploring monocular panoptic 3d scene reconstruction for autonomous driving,” in 2024 International Conference on 3D Vision (3DV) . IEEE, 2024, pp. 1219–1228
2024
Cited alongside, same era.
2024
Later among the works it cites.
Y. Shi, K. Jiang, J. Li, Z. Qian, J. Wen, M. Yang, K. Wang, and D. Yang, “Grid-centric traffic scenario perception for autonomous driving: A comprehensive review,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–21, 2024
2024
Later among the works it cites.
K. Wang, Y. Wu, J. Cen, Z. Pan, X. Li, Z. Wang, Z. Cao, and G. Lin, “Self-supervised class-agnostic motion prediction with spatial and temporal consistency regularizations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 14 638–14 647
2024
Later among the works it cites.
A. Prutsch, H. Bischof, and H. Possegger, “Efficient motion prediction: A lightweight & accurate trajectory prediction model with fast training and inference speed,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 9411–9417
2024
Later among the works it cites.
2024
Later among the works it cites.
K. Wang, Y. Wu, Z. Pan, X. Li, K. Xian, Z. Wang, Z. Cao, and G. Lin, “Semi-supervised class-agnostic motion prediction with pseudo label regeneration and bevmix,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 6, 2024, pp. 5490–5498
2024
Later among the works it cites.
S. Fang, Z. Liu, M. Wang, C. Xu, Y. Zhong, and S. Chen, “Self-supervised bird’s eye view motion prediction with cross-modality signals,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 2, 2024, pp. 1726–1734
2024
Later among the works it cites.