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3D Single Object Tracking in Point Clouds: A Review 认领 引用
1
作者 Yihao Kuang Hong Zhang +2 位作者 Jiaqi Wang Lingyu Jin Bo Huang 《Computers, Materials & Continua》 SCIE EI 2026年第6期118-142,共25页
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 ... 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. 展开更多
关键词 Point cloud 3D single object tracking autonomous driving sparsity spatio-temporal feature
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Topology-aware universal adversarial attack on 3D object tracking 认领 引用 被引量：1
2
作者 Riran Cheng Xupeng Wang +1 位作者 Ferdous Sohel Hang Lei 《Visual Intelligence》 2023年第1期1-12,共12页
3D object tracking based on deep neural networks has a wide range of potential applications,such as autonomous driving and robotics.However,deep neural networks are vulnerable to adversarial examples.Traditionally,adv... 3D object tracking based on deep neural networks has a wide range of potential applications,such as autonomous driving and robotics.However,deep neural networks are vulnerable to adversarial examples.Traditionally,adversarial examples are generated by applying perturbations to individual samples,which requires exhaustive calculations for each sample and thereby suffers from low efficiency during malicious attacks.Hence,the universal adversarial perturbation has been introduced,which is sample-agnostic.The universal perturbation is able to make classifiers misclassify most samples.In this paper,a topology-aware universal adversarial attack method against 3D object tracking is proposed,which can lead to predictions of a 3D tracker deviating from the ground truth in most scenarios.Specifically,a novel objective function consisting of a confidence loss,direction loss and distance loss generates an atomic perturbation from a tracking template,and aims to fail a tracking task.Subsequently,a series of atomic perturbations are iteratively aggregated to derive the universal adversarial perturbation.Furthermore,in order to address the characteristic of permutation invariance inherent in the point cloud data,the topology information of the tracking template is employed to guide the generation of the universal perturbation,which imposes correspondences between consecutively generated perturbations.The generated universal perturbation is designed to be aware of the topology of the targeted tracking template during its construction and application,thus leading to superior attack performance.Experiments on the KITTI dataset demonstrate that the performance of 3D object tracking can be significantly degraded by the proposed method. 展开更多
关键词 Deep nerual networks 3D object tracking Adversarial attack Topology-aware universal perturbation
Augmented Reality Based on Object Recognition for Piping System Maintenance 认领 引用
3
作者 Ana Regina Mizrahy Cuperschmid Mariana Higashi Sakamoto 《Journal of Architectural Environment & Structural Engineering Research》 CAS 2021年第2期38-44,共7页
Augmented Reality(AR)applications can be used to improve tasks and mitigate errors during facilities operation and maintenance.This article presents an AR system for facility management using a three-dimensional(3D)ob... Augmented Reality(AR)applications can be used to improve tasks and mitigate errors during facilities operation and maintenance.This article presents an AR system for facility management using a three-dimensional(3D)object tracking method.Through spatial mapping,the object of interest,a pipe trap underneath a sink,is tracked and mixed onto the AR visualization.From that,the maintenance steps are transformed into visible and animated instructions.Although some tracking issues related to the component parts were observed,the designed AR application results demonstrated the potential to improve facility management tasks. 展开更多
关键词 Augmented reality 3D object tracking Maintenance Pipe Facility management
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3D Single Object Tracking with Multi-View Unsupervised Center Uncertainty Learning 认领 引用
4
作者 Chengpeng Zhong Hui Shuai +2 位作者 Jiaqing Fan Kaihua Zhang Qingshan Liu 《CAAI Artificial Intelligence Research》 2023年第1期45-54,共10页
Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of nois... Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of noise in the labeling.Therefore,directly regressing the center coordinates is not very reasonable.Existing methods usually use volumetric-based,point-based,and view-based methods,with a relatively single modality.In addition,the sampling strategies commonly used usually result in the loss of object information,and holistic and detailed information is beneficial for object localization.To address these challenges,we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker(MUT).MUT models the potential uncertainty of center coordinates localization using an unsupervised manner,allowing the model to learn the true distribution.By projecting point clouds,MUT can obtain multi-view depth map features,realize efficient knowledge transfer from 2D to 3D,and provide another modality information for the tracker.We also propose a former attraction probability sampling strategy that preserves object information.By using both holistic and detailed descriptors of point clouds,the tracker can have a more comprehensive understanding of the tracking environment.Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8%and 0.6%in precision and success rate,respectively,and on the NuScenes dataset by 1.4%,and 6.1%in precision and success rate,respectively.The code is made available at http://gffzz188fe103f8f1460aswqvub9obuqux690w.ffgz.tsg.suse.edu.cn/abchears/MUT.git. 展开更多
关键词 3D single object tracking uncertainty modeling multi-view feature holistic and detailed descriptor
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