论文标题
LaserFlow：高效且概率的对象检测和运动预测
LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting
论文作者
论文摘要
在这项工作中，我们提出了LaserFlow，这是一种有效的方法，用于从激光雷达（LiDar）进行3D对象检测和运动预测。与以前的工作不同，我们的方法利用了LiDAR的天然范围视图表示，这使我们的方法可以实时在传感器的整个范围内操作，而无需脱氧或压缩数据。我们提出了一种新的多扫融合体系结构，该体系结构直接从范围图像中提取和合并时间特征。此外，我们提出了一种新型技术，用于学习受课程学习启发的未来轨迹的概率分布。我们在两个自动驾驶数据集上评估了LaserFlow，与现有的最新方法相比，我们证明了竞争结果。
In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR. Unlike the previous work, our approach utilizes the native range view representation of the LiDAR, which enables our method to operate at the full range of the sensor in real-time without voxelization or compression of the data. We propose a new multi-sweep fusion architecture, which extracts and merges temporal features directly from the range images. Furthermore, we propose a novel technique for learning a probability distribution over future trajectories inspired by curriculum learning. We evaluate LaserFlow on two autonomous driving datasets and demonstrate competitive results when compared to the existing state-of-the-art methods.
