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In this work, we present LaserFlow, an efficient method for 3D objectdetection and motion forecasting from LiDAR. Unlike the previous work, ourapproach utilizes the native range view representation of the LiDAR, whichenables our method to operate at the full range of the sensor in real-timewithout voxelization or compression of the data. We propose a new multi-sweepfusion architecture, which extracts and merges temporal features directly fromthe range images. Furthermore, we propose a novel technique for learning aprobability distribution over future trajectories inspired by curriculumlearning. We evaluate LaserFlow on two autonomous driving datasets anddemonstrate competitive results when compared to the existing state-of-the-artmethods.

LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting