Instance and Foreground Aware Single-Stage Outdoor 3D Object Detector from Point Clouds-Reference-Cited by-同舟云学术

Instance and Foreground Aware Single-Stage Outdoor 3D Object Detector from Point Clouds

Published:2023-10-20 Issue: Volume:34 Page:35-40
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
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Author:

Chen Yidong¹^ORCID,Cai Guorong¹^ORCID,Liu Zhaoliang¹^ORCID,Zeng Binghui¹^ORCID,Wang Zongyue¹^ORCID

Affiliation:

1. Computer Engineering College, China

Funder

the Science and Technology Program of Xiamen, China

National Natural Science Foundation of China

the Natural Science Foundation of Fujian Province, China

the Key Project of the Natural Science Foundation of Fujian Province

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650400.3650407

Reference21 articles.

1. Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan, and Y. Guo, 2022, “Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18 953–18 962.

2. Y. Zhou and O. Tuzel, 2018, June, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition. (CVPR), pp. 4490–4499.

3. Fast R-CNN

4. A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, 2019, June, “Pointpillars: Fast encoders for object detection from point clouds,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition. (CVPR), pp. 12 697–12 705.

5. C. He, H. Zeng, J. Huang, X. Hua, and L. Zhang, 2020, June, “Structure aware single-stage 3d object detection from point cloud,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition. (CVPR), pp. 11 870–11 879.