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3D point-cloud-based perception is a challenging but crucial computer vision task.
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B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d object detection from point clouds,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 7652–7660
2018
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2018
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2018
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Q. Huang, W. Wang, and U. Neumann, “Recurrent slice networks for 3d segmentation of point clouds,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2626–2635
2018
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2018
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2018
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2018
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2019
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2019
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2019
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2020
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H. Tang, Z. Liu, S. Zhao, Y. Lin, J. Lin, H. Wang, and S. Han, “Searching efficient 3d architectures with sparse point-voxel convolution,” in European Conference on Computer Vision . Springer, 2020, pp. 685–702
2020
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2020
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C. Xu, B. Wu, Z. Wang, W. Zhan, P. Vajda, K. Keutzer, and M. Tomizuka, “Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation,” in European Conference on Computer Vision . Springer, 2020, pp. 1–19
2020
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Z. Liu, H. Hu, Y. Cao, Z. Zhang, and X. Tong, “A closer look at local aggregation operators in point cloud analysis,” in European Conference on Computer Vision . Springer, 2020, pp. 326–342
2020
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N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in European Conference on Computer Vision . Springer, 2020, pp. 213–229
2020
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2020
Later among the works it cites.
2020
Later among the works it cites.
A. Jaegle, F. Gimeno, A. Brock, A. Zisserman, O. Vinyals, and J. Carreira, “Perceiver: General perception with iterative attention,” 2021
2021
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