３D目标检测论文汇总

从经典到最新

1. 将点云投影为图片后做用深度学习做检测

[4] B. Li, T. Zhang, and T. Xia, “Vehicle detection from 3d lidar usingfully convolutional network,” inProceedings of Robotics: Science andSystems, AnnArbor, Michigan, June 2016.

[5] X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d objectdetection network for autonomous driving,” inIEEE CVPR, vol. 1,no. 2, 2017, p. 3.

[6] J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint3d proposal generation and object detection from view aggregation,”in2018 IEEE/RSJ International Confere

2. 将点云投影为voxel后做用深度学习做检测

[7]B. Li, “3d fully convolutional network for vehicle detection in pointcloud,” inIntelligent Robots and Systems (IROS), 2017 IEEE/RSJInternational Conference on. IEEE, 2017, pp. 1513–1518.

[8] D. Z. Wang and I. Posner, “Voting for voting in online point cloudobject detection.” inRobotics: Science and Systems, vol. 1, no. 3,2015.

[9] B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d objectdetection from point clouds,” inThe IEEE Conference on ComputerVision and Pattern Recognition (CVPR), June 2018.

[10] M. Liang, B. Yang, S. Wang, and R. Urtasun, “Deep continuous fusionfor multi-sensor 3d object detection,” inProceedings of the EuropeanConference on Computer Vision (ECCV), 2018, pp. 641–656

3. 直接从点云提取特征

基于pointnet
[11] C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnetsfor 3d object detection from rgb-d data,” inProceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2018, pp.918–927

4. 直接从voxel提取特征

[14] Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloudbased 3d object detection,” inProceedings of the IEEE Conference onComputer Vision and Pattern Recognition, 2018, pp. 4490–4499.

[15] Y. Yan, Y. Mao, and B. Li, “Second: Sparsely embedded convolutionaldetection,”Sensors, vol. 18, no. 10, p. 3337, 2018.

[16] A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom,“Pointpillars: Encoders for object detection from point clouds,”arXivpreprint arXiv:1812.05784, 2018.