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This paper introduces InverseMatrixVT3D, an efficient method for transforming multi-view image features into 3D feature volumes for 3D semantic occupancy prediction.
A. Buluç, J. T. Fineman, M. Frigo, J. R. Gilbert, and C. E. Leiserson, “Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks,” in Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures , 2009, pp. 233–244
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2117–2125
2017
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T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
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J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
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M. Berman, A. R. Triki, and M. B. Blaschko, “The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4413–4421
2018
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J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 9297–9307
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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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
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2020
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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: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16 . Springer, 2020, pp. 194–210
2020
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Z. Murez, T. Van As, J. Bartolozzi, A. Sinha, V. Badrinarayanan, and A. Rabinovich, “Atlas: End-to-end 3d scene reconstruction from posed images,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VII 16 . Springer, 2020, pp. 414–431
2020
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L. Roldao, R. de Charette, and A. Verroust-Blondet, “Lmscnet: Lightweight multiscale 3d semantic completion,” in 2020 International Conference on 3D Vision (3DV) . IEEE, 2020, pp. 111–119
2020
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J. Li, K. Han, P. Wang, Y. Liu, and X. Yuan, “Anisotropic convolutional networks for 3d semantic scene completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 3351–3359
2020
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X. Chen, K.-Y. Lin, C. Qian, G. Zeng, and H. Li, “3d sketch-aware semantic scene completion via semi-supervised structure prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4193–4202
2020
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2021
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A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall, “Fiery: Future instance prediction in bird’s-eye view from surround monocular cameras,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 273–15 282
2021
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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
A. Schmied, T. Fischer, M. Danelljan, M. Pollefeys, and F. Yu, “R3d3: Dense 3d reconstruction of dynamic scenes from multiple cameras,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3216–3226
2023
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Y. Li, Z. Ge, G. Yu, J. Yang, Z. Wang, Y. Shi, J. Sun, and Z. Li, “Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1477–1485
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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9223–9232
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2021
Cited alongside, same era.
T. Wang, X. Zhu, J. Pang, and D. Lin, “Fcos3d: Fully convolutional one-stage monocular 3d object detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 913–922
2021
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S. F. Bhat, I. Alhashim, and P. Wonka, “Adabins: Depth estimation using adaptive bins,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4009–4018
2021
Cited alongside, same era.
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai, “Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,” in ECCV . Springer, 2022, pp. 1–18
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Q. Li, Y. Wang, Y. Wang, and H. Zhao, “Hdmapnet: An online hd map construction and evaluation framework,” in 2022 ICRA . IEEE, 2022, pp. 4628–4634
2022
Cited alongside, same era.
A. K. Akan and F. Güney, “Stretchbev: Stretching future instance prediction spatially and temporally,” in ECCV . Springer, 2022, pp. 444–460
2022
Cited alongside, same era.
A.-Q. Cao and R. de Charette, “Monoscene: Monocular 3d semantic scene completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 3991–4001
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
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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2023
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T. V. J.-H. K. Myeongjin and K. S. J. S.-G. Jeong, “Milo: Multi-task learning with localization ambiguity suppression for occupancy prediction cvpr 2023 occupancy challenge report,” 2023
2023
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2023
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2023
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2023
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X. Liu, H. Peng, N. Zheng, Y. Yang, H. Hu, and Y. Yuan, “Efficientvit: Memory efficient vision transformer with cascaded group attention,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 14 420–14 430
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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Y. Li, H. Bao, Z. Ge, J. Yang, J. Sun, and Z. Li, “Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 1486–1494
2023
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