论文标题
颞唇框架预测自动驾驶
Temporal LiDAR Frame Prediction for Autonomous Driving
论文作者
论文摘要
对于许多领域（例如自动驾驶和机器人技术），预期在动态场景中的未来至关重要。在本文中，我们提出了一类新型的神经网络体系结构，以预测以前的激光镜头。由于本应用程序中的地面真相仅仅是序列中的下一个框架，因此我们可以以自我监督的方式训练我们的模型。我们提出的架构基于Flownet3D和动态图CNN。我们使用倒角距离（CD）和Earth Mover的距离（EMD）作为损失函数和评估指标。我们使用新发布的Nuscenes数据集训练和评估我们的模型，并表征其与多个基线的性能和复杂性。与直接使用FlownET3D相比，我们提出的架构达到CD和EMD几乎低一个数量级。此外，我们表明我们的预测会产生合理的场景流近似值，而无需使用任何标记的监督。
Anticipating the future in a dynamic scene is critical for many fields such as autonomous driving and robotics. In this paper we propose a class of novel neural network architectures to predict future LiDAR frames given previous ones. Since the ground truth in this application is simply the next frame in the sequence, we can train our models in a self-supervised fashion. Our proposed architectures are based on FlowNet3D and Dynamic Graph CNN. We use Chamfer Distance (CD) and Earth Mover's Distance (EMD) as loss functions and evaluation metrics. We train and evaluate our models using the newly released nuScenes dataset, and characterize their performance and complexity with several baselines. Compared to directly using FlowNet3D, our proposed architectures achieve CD and EMD nearly an order of magnitude lower. In addition, we show that our predictions generate reasonable scene flow approximations without using any labelled supervision.
