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Monocular Semantic Occupancy Prediction aims to infer the complete 3D geometry and semantic information of scenes from only 2D images.
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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
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2020
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M. Popovic, F. Thomas, S. Papatheodorou, N. Funk, T. Vidal-Calleja, and S. Leutenegger, “Volumetric occupancy mapping with probabilistic depth completion for robotic navigation,” in IEEE Robotics and Automation Letters , 2021, pp. 5072–5079
2021
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H. Li, Y. Chen, Q. Zhang, and D. Zhao, “Bifnet: Bidirectional fusion network for road segmentation,” IEEE transactions on cybernetics , vol. 52, no. 9, pp. 8617–8628, 2021
2021
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2021
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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 , 2021
2021
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Z. Yan, X. Li, K. Wang, Z. Zhang, J. Li, and J. Yang, “Multi-modal masked pre-training for monocular panoramic depth completion,” in European Conference on Computer Vision . Springer, 2022, pp. 378–395
2022
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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
2022
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——, “Rignet: Repetitive image guided network for depth completion,” in European Conference on Computer Vision . Springer, 2022, pp. 214–230
2022
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Z. Xia, Y. Liu, X. Li, X. Zhu, Y. Ma, Y. Li, Y. Hou, and Y. Qiao, “Scpnet: Semantic scene completion on point cloud,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
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2023
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B. Jin, X. Liu, Y. Zheng, P. Li, H. Zhao, T. Zhang, Y. Zheng, G. Zhou, and J. Liu, “Adapt: Action-aware driving caption transformer,” 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023
2023
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Z. Yan, K. Wang, X. Li, Z. Zhang, J. Li, and J. Yang, “Desnet: Decomposed scale-consistent network for unsupervised depth completion,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 3, 2023, pp. 3109–3117
2023
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Y. Li, Z. Yu, C. Choy, C. Xiao, J. M. Alvarez, S. Fidler, C. Feng, and A. Anandkumar, “Voxformer: Sparse voxel transformer for camera-based 3d semantic scene completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023
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2023
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2023
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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
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 , 2023
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
2023
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Y. Wei, L. Zhao, W. Zheng, Z. Zhu, Y. Rao, G. Huang, J. Lu, and J. Zhou, “Surrounddepth: Entangling surrounding views for self-supervised multi-camera depth estimation,” in Conference on Robot Learning , 2023
2023
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H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao, and A. Geiger, “Unifying flow, stereo and depth estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
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