A Small-Size 3d Object Detection Network for Analyzing the Sparsity of Raw Lidar Point Cloud
Author:
Publisher
Springer Science and Business Media LLC
Subject
Engineering (miscellaneous),Atomic and Molecular Physics, and Optics
Link
https://link.springer.com/content/pdf/10.1007/s10946-023-10173-3.pdf
Reference14 articles.
1. Y. Zhou and O. Tuzel, “VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection,” in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2018), pp. 4490–4499.
2. Y. Yan, Y. Mao, and B. Li, Second: Sparsely embedded convolutional detection[J]. Sensors, 18, 3337 (2018); DOI: https://doi.org/10.3390/s18103337
3. T. Yin and X. Zhou, “Center-Based 3D Object Detection and Tracking,” in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2021), pp. 11784–11793;
4. J. Deng, S. Shi, P. Li, et al., “Voxel R-CNN: Towards High Performance Voxel-Based 3D Object Detection,” arXiv:2012.15712 [cs.CV] (2020).
5. J. Mao, Y. Xue, M. Niu, et al., "Voxel Transformer for 3D Object Detection," in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), (2021), pp. 3164-3173
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