Point Cloud Feature Fusion is a methodological framework within multimodal data integration that specifically combines geometric and semantic features extracted from multiple point cloud representations to enhance 3D perception accuracy by preserving spatial coherence and structural detail.
164
Publications
11K
Citations
726
Authors
214
Institutions
Publications per year
2017–2026
157
Parents
Publications
164
PointAugmenting: Cross-Modal Augmentation for 3D Object Detection
Chunwei Wang, Chao Ma, Ming Zhu et al. · 2021 · 449 citations
Yilun Chen, Shu Liu, Xiaoyong Shen et al. · 2019 · 424 citations
Multi-level Fusion Based 3D Object Detection from Monocular Images
Bin Xu, Zhenzhong Chen · 2018 · 402 citations
1–5 of 164
Authors
726
- JL
3
h-index
- YC
3
h-index
- RJ
3
h-index
- HY
3
h-index
- ZF
3
h-index
1–5 of 726
Institutions
214
Tsinghua University
Beijing, China
9 h-index
31 publications
2.3K citations
Chinese Academy of Sciences
Beijing, China
8 h-index
28 publications
1.2K citations
Huazhong University of Science and Technology
Wuhan, China
7 h-index
25 publications
1.3K citations
Shanghai Jiao Tong University
Shanghai, China
6 h-index
18 publications
2.7K citations
Wuhan University
Wuhan, China
6 h-index
17 publications
1.6K citations
Xiamen University
Xiamen, China
6 h-index
20 publications
1.2K citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
14 publications
582 citations
Tongji University
Shanghai, China
4 h-index
11 publications
518 citations
1–8 of 214
Venues
- arXiv (Cornell University)
Repository · 19 publications · 511 citations
13
h-index
- Sensors
7 publications · 146 citations
7
h-index
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference · 5 publications · 924 citations
5
h-index
- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 4 publications · 449 citations
4
h-index
- IEEE Access
4 publications · 214 citations
4
h-index
1–5