论文标题
端到端3D多对象跟踪和轨迹预测
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting
论文作者
论文摘要
3D多对象跟踪（MOT）和轨迹预测是现代3D感知系统中的两个关键组成部分。我们假设在一个框架下统一两个任务以学习代理交互的共享特征表示是有益的。为了评估这一假设，我们为3D MOT和轨迹预测提出了一个统一的解决方案，该解决方案还结合了两个新的新型计算单元。首先，我们通过引入图形神经网络（GNN）来捕获多个代理相互作用的方式来采用特征交互技术。 GNN能够对复杂的层次相互作用进行建模，改善MOT关联的判别特征学习，并为轨迹预测提供社会意识的背景。其次，我们使用多样性抽样函数来提高预测轨迹的质量和多样性。对学习的采样函数进行了训练，可以从生成轨迹分布中有效提取各种结果，并有助于避免生成许多重复的轨迹样品的问题。我们表明我们的方法在Kitti数据集上实现了最先进的性能。我们的项目网站位于http://www.xinshuoweng.com/projects/gnntrkforecast。
3D multi-object tracking (MOT) and trajectory forecasting are two critical components in modern 3D perception systems. We hypothesize that it is beneficial to unify both tasks under one framework to learn a shared feature representation of agent interaction. To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units. First, we employ a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which multiple agents interact with one another. The GNN is able to model complex hierarchical interactions, improve the discriminative feature learning for MOT association, and provide socially-aware context for trajectory forecasting. Second, we use a diversity sampling function to improve the quality and diversity of our forecasted trajectories. The learned sampling function is trained to efficiently extract a variety of outcomes from a generative trajectory distribution and helps avoid the problem of generating many duplicate trajectory samples. We show that our method achieves state-of-the-art performance on the KITTI dataset. Our project website is at http://www.xinshuoweng.com/projects/GNNTrkForecast.
