7 citations · 19 across the 11 of their papers we have counts for
11 papers
TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking
Zhaofeng Hu, Sifan Zhou, Jiahao Nie +3
LiDAR-based 3D Single Object Tracking (3D SOT) is critical for robotic perception and navigation and aims to localize dynamic objects across frames in sparse point clouds. Existing…
A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life
Chenhan Wang, Zhengyi Bao, Huipin Lin +2
Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicl…
FocusTrack: One-Stage Focus-and-Suppress Framework for 3D Point Cloud Object Tracking
Sifan Zhou, Jiahao Nie, Ziyu Zhao +2
In 3D point cloud object tracking, the motion-centric methods have emerged as a promising avenue due to its superior performance in modeling inter-frame motion. However, existing t…
VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
Yuxuan Lu, Jiahao Nie, Zhiwei He +2
Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network. Despite demonstrated success, such networks suffer fro…
P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds
Jiahao Nie, Fei Xie, Sifan Zhou +3
3D single object tracking (SOT) methods based on appearance matching has long suffered from insufficient appearance information incurred by incomplete, textureless and semantically…
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Jiahao Nie, Zhiwei He, Xudong Lv +3
Category-specific models are provenly valuable methods in 3D single object tracking (SOT) regardless of Siamese or motion-centric paradigms. However, such over-specialized model de…
Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation
Yun Xing, Jian Kang, Aoran Xiao +3
Vision-Language Pre-training has demonstrated its remarkable zero-shot recognition ability and potential to learn generalizable visual representations from language supervision. Ta…
OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking
Jiahao Nie, Zhiwei He, Yuxiang Yang +3
Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-…
GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss
Jiahao Nie, Zhiwei He, Yuxiang Yang +3
Siamese trackers based on 3D region proposal network (RPN) have shown remarkable success with deep Hough voting. However, using a single seed point feature as the cue for voting fa…
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
Jiahao Nie, Zhiwei He, Yuxiang Yang +2
Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset…
Learning Localization-aware Target Confidence for Siamese Visual Tracking
Jiahao Nie, Han Wu, Zhiwei He +3
Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by the classification and regression. While such a paradigm…