用一个多功能变压器重新审视基于罗的3D单个对象跟踪

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Updated: Sep 19, 2025

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Jiaming Liu, Yue Wu, Qiguang Miao

IEEE transactions on pattern analysis and machine intelligence

|June 19, 2025

概括

一种新的多功能点跟踪变压器 (VPTT) 方法通过使用变压器启发的建模来增强3D单个对象跟踪 (SOT). 这种方法可以提高复杂,稀疏的点云数据的跟踪精度和效率.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器人技术机器人技术机器人技术

背景情况:

对于自动驾驶和规划等应用来说,3D单个对象跟踪 (SOT) 是至关重要的.
3D SOT的挑战包括稀疏的点云和复杂的影响因素.
现有的方法很难有效地处理3D空间数据的复杂性.

研究的目的:

为有效的3D SOT提出一种全新的多功能点跟踪变压器 (VPTT) 方法.
为了利用变压器架构,在稀疏的3D点云中改进对象跟踪.
为3D跟踪任务开发一个多功能和高效的端到端可训练模型.

主要方法:

一个基于Siamese的跟踪范式,利用对象引导从模板到搜索点云.
在深度变压器网络 (VerFormer) 中实施自我和交叉注意力机制.
整合了四个专门的匹配操作和中间输出预测 (面具概率,距离,方向角度).

主要成果:

在具有挑战性的基准 (KITTI,nuScenes,Waymo) 上,VPTT展示了最先进的跟踪性能.
该方法实现了高效率,在基于米的跟踪上运行约62 FPS.
专门的设计使得3D跟踪的有效端到端培训成为可能.

结论:

拟议的VPTT方法在3D单个对象跟踪方面取得了重大进展.
基于变压器的方法在跟踪中处理稀疏点云数据方面非常有效.
VPTT为现实世界3D SOT应用提供了一种多功能和高效的解决方案.

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