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Camera and 3D LiDAR sensors have become indispensable devices in modern autonomous driving vehicles, where the camera provides the fine-grained texture, color information in 2D space and LiDAR captures more precise and farther-away distance measurements of the surrounding environments.
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L. Han, T. Zheng, L. Xu, and L. Fang, “Occuseg: Occupancy-aware 3d instance segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 2937–2946
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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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 621–11 631
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2020
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L. Zhao and W. Tao, “Jsnet: Joint instance and semantic segmentation of 3d point clouds,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 951–12 958
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S. Vora, A. H. Lang, B. Helou, and O. Beijbom, “Pointpainting: Sequential fusion for 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 4604–4612
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2020
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H. Zhou, X. Zhu, X. Song, Y. Ma, Z. Wang, H. Li, and D. Lin, “Cylinder3d: An effective 3d framework for driving-scene lidar semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2021
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
S. Qiu, S. Anwar, and N. Barnes, “Geometric back-projection network for point cloud classification,” IEEE Transactions on Multimedia , pp. 1–1, 2021
2021
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Z. Fu and W. Hu, “Dynamic point cloud inpainting via spatial-temporal graph learning,” IEEE Transactions on Multimedia , pp. 1–1, 2021
2021
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M. Fan, S. Lai, J. Huang, X. Wei, Z. Chai, J. Luo, and X. Wei, “Rethinking bisenet for real-time semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 9716–9725
2021
Closest in time.