EFFOcc: Learning Efficient Occupancy Networks from Minimal Labels for Autonomous Driving

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Z. Yu, C. Shu, J. Deng, K. Lu, Z. Liu, J. Yu, D. Yang, H. Li, and Y. Chen, “Flashocc: Fast and memory-efficient occupancy prediction via channel-to-height plugin,” arXiv preprint arXiv:2311.12058 , 2023

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2023

Cited alongside, same era.

S. Zhou, W. Liu, C. Hu, S. Zhou, and C. Ma, “Unidistill: A universal cross-modality knowledge distillation framework for 3d object detection in bird’s-eye view,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023

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Z. Wang, D. Li, C. Luo, C. Xie, and X. Yang, “Distillbev: Boosting multi-camera 3d object detection with cross-modal knowledge distillation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8637–8646

J. Hou, X. Li, W. Guan, G. Zhang, D. Feng, Y. Du, X. Xue, and J. Pu, “Fastocc: Accelerating 3d occupancy prediction by fusing the 2d bird’s-eye view and perspective view,” 2024

2024

Closest in time.

H. Liu, Y. Chen, H. Wang, Z. Yang, T. Li, J. Zeng, L. Chen, H. Li, and L. Wang, “Fully sparse 3d occupancy prediction,” in European Conference on Computer Vision . Springer, 2024, pp. 54–71

2024

Closest in time.

P. Tang, Z. Wang, G. Wang, J. Zheng, X. Ren, B. Feng, and C. Ma, “Sparseocc: Rethinking sparse latent representation for vision-based semantic occupancy prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 035–15 044

2024

Closest in time.

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

2024