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Grid-centric perception is a crucial field for mobile robot perception and navigation.
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X. Yan, J. Gao, J. Li, R. Zhang, Z. Li, R. Huang, and S. Cui, “Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 4, 2021, pp. 3101–3109
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2021
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2022
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2022
Later among the works it cites.
Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 786–12 796
2022
Later among the works it cites.
2022
Later among the works it cites.
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2021
Cited alongside, same era.
C. Luo, X. Yang, and A. Yuille, “Self-Supervised Pillar Motion Learning for Autonomous Driving,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , pp. 3182–3191, 2021
2021
Cited alongside, same era.
S. Casas, A. Sadat, and R. Urtasun, “Mp3: A unified model to map, perceive, predict and plan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 403–14 412
2021
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X. Chen, S. Xie, and K. He, “An Empirical Study of Training Self-Supervised Vision Transformers,” Proceedings of the IEEE International Conference on Computer Vision , pp. 9620–9629, 2021
2021
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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
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K. Rezaee, P. Yadmellat, and S. Chamorro, “Motion planning for autonomous vehicles in the presence of uncertainty using reinforcement learning,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 3506–3511
2021
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S. Richter, Y. Wang, J. Beck, S. Wirges, and C. Stiller, “Semantic evidential grid mapping using monocular and stereo cameras,” Sensors , vol. 21, no. 10, p. 3380, 2021
2021
Cited alongside, same era.
T. Q. Tran, A. Becker, and D. Grzechca, “Environment mapping using sensor fusion of 2d laser scanner and 3d ultrasonic sensor for a real mobile robot,” Sensors , vol. 21, no. 9, p. 3184, 2021
2021
Cited alongside, same era.
R. Van Kempen, B. Lampe, T. Woopen, and L. Eckstein, “A simulation-based end-to-end learning framework for evidential occupancy grid mapping,” in 2021 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2021, pp. 934–939
2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
Z. Li, W. Wang, E. Xie, Z. Yu, A. Anandkumar, J. M. Álvarez, T. Lu, and P. Luo, “Panoptic segformer: Delving deeper into panoptic segmentation with transformers,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1270–1279, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
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2023
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X. Wang, Z. Zhu, W. Xu, Y. Zhang, Y. Wei, X. Chi, Y. Ye, D. Du, J. Lu, and X. Wang, “Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 17 850–17 859
2023
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W. Tong, C. Sima, T. Wang, L. Chen, S. Wu, H. Deng, Y. Gu, L. Lu, P. Luo, D. Lin, et al. , “Scene as occupancy,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8406–8415
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2023
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2023
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——, “Lidar-based 4d occupancy completion and forecasting,” arXiv preprint arXiv:2310.11239 , 2023
2023
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2023
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2023
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Y. Zhang, Z. Zhu, and D. Du, “Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 9433–9443
2023
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Y. Wei, L. Zhao, W. Zheng, Z. Zhu, J. Zhou, and J. Lu, “Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 21 729–21 740
2023
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J. Yao, C. Li, K. Sun, Y. Cai, H. Li, W. Ouyang, and H. Li, “Ndc-scene: Boost monocular 3d semantic scene completion in normalized device coordinates space,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV) . IEEE Computer Society, 2023, pp. 9421–9431
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2023
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2023
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B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, pp. 1–14, 2023
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2023
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Z. Li, Z. Yu, W. Wang, A. Anandkumar, T. Lu, and J. M. Alvarez, “Fb-bev: Bev representation from forward-backward view transformations,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 6919–6928
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W. Wang, J. Dai, Z. Chen, Z. Huang, Z. Li, X. Zhu, X. Hu, T. Lu, L. Lu, H. Li, et al. , “Internimage: Exploring large-scale vision foundation models with deformable convolutions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 408–14 419
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A.-Q. Cao, A. Dai, and R. de Charette, “Pasco: Urban 3d panoptic scene completion with uncertainty awareness,” 2023
2023
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2023
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P. Li, R. Zhao, Y. Shi, H. Zhao, J. Yuan, G. Zhou, and Y.-Q. Zhang, “Lode: Locally conditioned eikonal implicit scene completion from sparse lidar,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 8269–8276
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H. Zhou, Z. Ge, Z. Li, and X. Zhang, “Matrixvt: Efficient multi-camera to bev transformation for 3d perception,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8548–8557
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J. Lee, J. Koh, Y. Lee, and J. W. Choi, “D-align: Dual query co-attention network for 3d object detection based on multi-frame point cloud sequence,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9238–9244
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2023
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S. Fang, Z. Wang, Y. Zhong, J. Ge, and S. Chen, “Tbp-former: Learning temporal bird’s-eye-view pyramid for joint perception and prediction in vision-centric autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1368–1378
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2023
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2023
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2023
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2023
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2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, et al. , “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
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2023
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X. Dong, J. Bao, Y. Zheng, T. Zhang, D. Chen, H. Yang, M. Zeng, W. Zhang, L. Yuan, D. Chen, et al. , “Maskclip: Masked self-distillation advances contrastive language-image pretraining,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 10 995–11 005
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W. Tong, C. Sima, T. Wang, L. Chen, S. Wu, H. Deng, Y. Gu, L. Lu, P. Luo, D. Lin, et al. , “Scene as occupancy,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8406–8415
2023
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V. Dewangan, B. Sharma, T. Choudhary, S. Sharma, A. Aanegola, A. K. Singh, and K. M. Krishna, “Uap-bev: Uncertainty aware planning using bird’s eye view generated from surround monocular images,” in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) , 2023, pp. 1–8
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M. Cho, Y. Lee, and K.-S. Kim, “Model predictive control of autonomous vehicles with integrated barriers using occupancy grid maps,” IEEE Robotics and Automation Letters , vol. 8, no. 4, pp. 2006–2013, 2023
2023
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Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, L. Lu, X. Jia, Q. Liu, J. Dai, Y. Qiao, and H. Li, “Planning-oriented autonomous driving,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 17 853–17 862
2023
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2023
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H. Liu, Z. Huang, and C. Lv, “Occupancy prediction-guided neural planner for autonomous driving,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2023, pp. 4859–4865
2023
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G. Chen, Y. Zhang, and X. Li, “Attention-based highway safety planner for autonomous driving via deep reinforcement learning,” IEEE Transactions on Vehicular Technology , 2023
2023
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Z. Wen, Y. Zhang, X. Chen, J. Wang, Y.-H. Li, and Y.-K. Huang, “Tofg: Temporal occupancy flow graph for prediction and planning in autonomous driving,” IEEE Transactions on Intelligent Vehicles , 2023
2023
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2023
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2023
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2023
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2023
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2023
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T. Zhou, J. Chen, Y. Shi, K. Jiang, M. Yang, and D. Yang, “Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection,” IEEE Transactions on Intelligent Vehicles , pp. 1–14, 2023
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2023
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2023
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Y. Zhang, J. Zhang, Z. Wang, J. Xu, and D. Huang, “Vision-based 3d occupancy prediction in autonomous driving: a review and outlook,” 2024
2024
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H. Xu, J. Chen, S. Meng, Y. Wang, and L.-P. Chau, “A survey on occupancy perception for autonomous driving: The information fusion perspective,” 2024
2024
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X. Tian, T. Jiang, L. Yun, Y. Mao, H. Yang, Y. Wang, Y. Wang, and H. Zhao, “Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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J. Pan, Z. Wang, and L. Wang, “Co-occ: Coupling explicit feature fusion with volume rendering regularization for multi-modal 3d semantic occupancy prediction,” IEEE Robotics and Automation Letters , 2024
2024
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2024
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B. Li, Y. Sun, Z. Liang, D. Du, Z. Zhang, X. Wang, Y. Wang, X. Jin, and W. Zeng, “Bridging stereo geometry and bev representation with reliable mutual interaction for semantic scene completion,” 2024
2024
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2024
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H. Zhang, X. Yan, D. Bai, J. Gao, P. Wang, B. Liu, S. Cui, and Z. Li, “Radocc: Learning cross-modality occupancy knowledge through rendering assisted distillation,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 7, 2024, pp. 7060–7068
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2024
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2024
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F. Ding, X. Wen, Y. Zhu, Y. Li, and C. X. Lu, “Radarocc: Robust 3d occupancy prediction with 4d imaging radar,” 2024
2024
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2024
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2024
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2024
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2024
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L. Zhang, Y. Xiong, Z. Yang, S. Casas, R. Hu, and R. Urtasun, “Copilot4d: Learning unsupervised world models for autonomous driving via discrete diffusion,” in The Twelfth International Conference on Learning Representations , 2024
2024
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C. Min, L. Xiao, D. Zhao, Y. Nie, and B. Dai, “Multi-camera unified pre-training via 3d scene reconstruction,” IEEE Robotics and Automation Letters , 2024
2024
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2024
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2024
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A. Vobecky, O. Siméoni, D. Hurych, S. Gidaris, A. Bursuc, P. Pérez, and J. Sivic, “Pop-3d: Open-vocabulary 3d occupancy prediction from images,” Advances in Neural Information Processing Systems , vol. 36, 2024
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2024
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2024
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2024
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2024
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2024
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2024
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LightwheelAI and L. contributors, “Lightwheelocc: A 3d occupancy synthetic dataset in autonomous driving,” https://github.com/OpenDriveLab/LightwheelOcc , 2024
2024
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O. contributors, “Video generation models as world simulators,” https://openai.com/index/video-generation-models-as-world-simulators/ , 2024
2024
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L. Wang, W. Zheng, Y. Ren, H. Jiang, Z. Cui, H. Yu, and J. Lu, “Occsora: 4d occupancy generation models as world simulators for autonomous driving,” 2024
2024
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D. Isele, R. Rahimi, A. Cosgun, K. Subramanian, and K. Fujimura, “Navigating occluded intersections with autonomous vehicles using deep reinforcement learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 2034–2039
2039
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