7 citations · 13 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
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…