论文标题
速度与激情：实时端到端3D检测，跟踪和运动预测，并通过单个卷积网进行预测
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
论文作者
论文摘要
在本文中，我们提出了一个新型的深神经网络，该网络能够共同理解3D检测，跟踪和运动预测，并给定由3D传感器捕获的数据。通过共同推理这些任务，我们的整体方法对遮挡和稀疏数据在范围内更为强大。我们的方法在3D世界的鸟类视图表示上进行了跨空间和时间的3D卷积，这在记忆和计算方面非常有效。我们在北美几个城市捕获的一个新的非常大规模数据集上的实验表明，我们可以大大优于最先进的空间。重要的是，通过共享计算，我们可以在短短30毫秒内执行所有任务。
In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. Our approach performs 3D convolutions across space and time over a bird's eye view representation of the 3D world, which is very efficient in terms of both memory and computation. Our experiments on a new very large scale dataset captured in several north american cities, show that we can outperform the state-of-the-art by a large margin. Importantly, by sharing computation we can perform all tasks in as little as 30 ms.
