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.

中文翻译：

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LaserFlow：高效的概率目标检测和运动预测

在这项工作中，我们提出了 LaserFlow，这是一种从 LiDAR 进行 3D 对象检测和运动预测的有效方法。与之前的工作不同，我们的方法利用了 LiDAR 的原生范围视图表示，这使我们的方法能够在传感器的整个范围内实时运行，而无需对数据进行体素化或压缩。我们提出了一种新的多扫描融合架构，它直接从距离图像中提取和合并时间特征。此外，我们提出了一种新技术，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 LaserFlow，并展示了与现有最​​先进方法相比的竞争结果。