128 citations · 161 across the 7 of their papers we have counts for
7 papers
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
Xiyang Wang, Chunyun Fu, Jiawei He +6
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve…
3D Multi-Object Tracking Based on Uncertainty-Guided Data Association
Jiawei He, Chunyun Fu, Xiyang Wang
In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calcul…
DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association
Xiyang Wang, Chunyun Fu, Zhankun Li +2
In the recent literature, on the one hand, many 3D multi-object tracking (MOT) works have focused on tracking accuracy and neglected computation speed, commonly by designing rather…
Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views
Zhizhong Han, Xiyang Wang, Yu-Shen Liu +1
Unsupervised learning of global features for 3D shape analysis is an important research challenge because it avoids manual effort for supervised information collection. In this pap…
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Zhizhong Han, Xiyang Wang, Yu-Shen Liu +1
Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from…
3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention
Zhizhong Han, Xiyang Wang, Chi-Man Vong +3
Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling…
Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences
Zhizhong Han, Mingyang Shang, Xiyang Wang +2
A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels.…