3D Object Detection Fusion
3D Object Detection Fusion is a methodological framework within object detection fusion that integrates complementary spatial and semantic information from multiple sensors or modalities to jointly infer the precise three-dimensional location, orientation, and class of objects in a scene.
205
Publications
21.5K
Citations
770
Authors
215
Institutions
Publications per year
2017–2026
202
Parents
205
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla et al. · 2020 · 3K citations
PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection
Shaoshuai Shi, Chaoxu Guo, Li Jiang et al. · 2020 · 2.1K citations
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Ming Liang, Bin Yang, Yun Chen et al. · 2019 · 761 citations
1–5 of 205
770
7
h-index
6
h-index
Korea Advanced Institute of Science and Technology, South Korea
5 publications · 333 citations
6
h-index
5
h-index
5
h-index
1–5 of 770
215
Tsinghua University
Beijing, China
14 h-index
59 publications
4.3K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
10 h-index
27 publications
9.5K citations

Beihang University
Beijing, China
9 h-index
14 publications
1.2K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
22 publications
897 citations
University of Science and Technology of China
Hefei, China
7 h-index
35 publications
2.9K citations

Shanghai Jiao Tong University
Shanghai, China
7 h-index
19 publications
2.7K citations

Harbin Institute of Technology
Harbin, China
6 h-index
16 publications
943 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
6 h-index
9 publications
2.8K citations
1–8 of 215
Repository · 28 publications · 1.9K citations
19
h-index
8 publications · 229 citations
10
h-index
8 publications · 1.3K citations
9
h-index
Conference · 7 publications · 1.7K citations
7
h-index
Conference · 7 publications · 538 citations
7
h-index
1–5