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In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes compared with traditional perception tasks, such as 3D object detection and bird's-eye view (BEV) semantic segmentation.
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H. Vanholder, “Efficient inference with tensorrt,” in GPU Technology Conference , vol. 1, 2016, p. 2
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 2016, pp. 770–778
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T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 . IEEE Computer Society, 2017, pp. 936–944
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2017
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J. Philion and S. Fidler, “Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d,” in Computer Vision – ECCV 2020 , A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds. Cham: Springer International Publishing, 2020, vol. 12359, pp. 194–210
2020
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Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. E. Campbell, and K. Q. Weinberger, “Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020
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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 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020 . IEEE, 2020, pp. 11 618–11 628
2020
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2021
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2021
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T. Wang, X. Zhu, J. Pang, and D. Lin, “FCOS3D: fully convolutional one-stage monocular 3d object detection,” in IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021, Montreal, BC, Canada, October 11-17, 2021 . IEEE, 2021, pp. 913–922
2021
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2022
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2022
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B. Zhou and P. Krähenbühl, “Cross-view transformers for real-time map-view semantic segmentation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022 . IEEE, 2022, pp. 13 750–13 759
2023
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2023
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2023
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Y. Huang, W. Zheng, Y. Zhang, J. Zhou, and J. Lu, “Tri-perspective view for vision-based 3d semantic occupancy prediction,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023 . IEEE, 2023, pp. 9223–9232
2023
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2022
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2022
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2022
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2022
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2022
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2022
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A. Cao and R. de Charette, “Monoscene: Monocular 3d semantic scene completion,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022 . IEEE, 2022, pp. 3981–3991
2022
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2022
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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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2023
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