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Open Access

REVIEW

3D Single Object Tracking in Point Clouds: A Review

Yihao Kuang1,2, Hong Zhang1,2, Jiaqi Wang1,2, Lingyu Jin1,2, Bo Huang1,2,*
1 College of Optoelectronic Engineering, Chongqing University, Chongqing, China
2 Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China
* Corresponding Author: Bo Huang. Email: email
(This article belongs to the Special Issue: Advances in Video Object Tracking: Methods, Challenges, and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076652

Received 24 November 2025; Accepted 30 January 2026; Published online 19 February 2026

Abstract

3D single object tracking (SOT) based on point clouds is a fundamental task for environmental perception in autonomous driving and dynamic scene understanding in robotics. Recent technological advancements in this field have significantly bolstered the environmental interaction capabilities of intelligent systems. This field faces persistent challenges, including feature degradation induced by point cloud sparsity, representation drift caused by non-rigid deformation, and occlusion in complex scenarios. Traditional appearance matching methods, particularly those relying on Siamese networks, are severely constrained by point cloud characteristics, often failing under rapid motions or structural ambiguities among similar objects. In response, the research paradigm has progressively evolved toward motion-centric modeling approaches. These emerging frameworks utilize spatio-temporal joint modeling and geometric shape completion to attain notable performance gains. Furthermore, the incorporation of attention mechanisms and State Space Model (SSM) has enabled more effective multi-scale spatio-temporal feature association, which is particularly beneficial for long-term tracking scenarios. To the best of our knowledge, this is the first comprehensive survey dedicated to 3D single object tracking in point clouds. We provide a detailed analysis of current tracking methods, scrutinizing their limitations regarding multi-object interference and analyzing the trade-off between accuracy and computational efficiency. Finally, we discuss potential future directions, including the development of lightweight models for edge deployment and the integration of cross-modal fusion strategies.

Keywords

Point cloud; 3D single object tracking; autonomous driving; sparsity; spatio-temporal feature

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