Efficient 3d point cloud object tracking without templates
TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Tracking moving objects in 3D using LiDAR is important for robots and self-driving cars, but existing methods are often slow because they compare new data with past templates. The authors found that just using the last known object position and simple geometry is enough to track objects accurately. They created a new system called TFTrack that does not use templates and runs much faster while still being accurate. This makes it easier to track objects in real time on devices with limited computing power.
What this means in practice
- •For autonomous vehicle developers: Implement fast and resource-efficient 3D object tracking for obstacle detection and navigation using LiDAR data without relying on costly template comparisons.$Commercial implications: Enables the creation of real-time embedded tracking systems for self-driving cars that reduce hardware costs and improve speed.
- •For robotics engineers: Deploy efficient 3D tracking algorithms on mobile robots to better track dynamic objects in dense and sparse environments using simplified motion cues.
Authors
Zhaofeng Hu, Sifan Zhou, Jiahao Nie, Ziyu Zhao, Weizi Li, Ci-jyun Liang
Abstract
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 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. Our in-depth analysis reveals: (i) the template paradigm is redundant, as the previous bounding box center encodes sufficient historical context; (ii) complex motion modeling is unnecessary, as geometric alignment provides adequate motion priors. Based on the above findings, we propose the first Template-Free Tracking framework (TFTrack). The novel framework eliminates the need for template-search pairings and operates directly on the current frame guided solely by the prior bounding box center and size. We instantiate this paradigm into three variants: TFTrack-Voxel, TFTrack-Pillar, and TFTrack-Point, to explore different 3D representations under a unified framework, ensuring flexibility across sparse and dense scenes. Extensive experiments on KITTI and nuScenes benchmarks show that TFTrack is competitive with leading template-based trackers, while reducing FLOPs by approximately 50% and running at approximately 120 FPS. By simplifying overcomplicated motion-centric designs, TFTrack establishes a new minimalist paradigm for efficient 3D point cloud tracking, paving the way for real-time and resource-efficient deployment in embedded robotic systems, such as autonomous vehicles. The code is available at https://github.com/tftrack-anonymous/TFTrack/tree/main.