TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking
Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.
TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.
- Shows the template paradigm is redundant; prior bounding box center suffices
- Three variants: TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point
- ~50% fewer FLOPs at ~120 FPS, competitive on KITTI and nuScenes
- Aimed at embedded robotic systems like autonomous vehicles
- Products
- TFTrack
From the original article
1 min read at arxiv.orgLiDAR-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 methods, rooted in the Siamese tracking paradigm from 2D vision, rely on costly dual-input designs and excessive motion modeling guided by template priors, hindering their efficiency.
By Zhaofeng Hu · arxiv.org
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