LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting
This addresses efficient perception for autonomous vehicles, though it appears incremental with competitive rather than breakthrough performance.
The authors tackled 3D object detection and motion forecasting from LiDAR by developing LaserFlow, which uses native range view representation and multi-sweep fusion to operate in real-time without data compression, achieving competitive results on autonomous driving datasets.
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.