(video) Point Cloud Forecasting in Autonomous Driving: Approach, Challenge & Benchmark, Xinshuo Weng

3D-DLAD workshop : https://sites.google.com/view/3d-dlad-v3-iv2021/schedule

Abstract: Perception and prediction pipeline (3D object detection and multi-object tracking, trajectory forecasting) is a key component in self-driving cars. Although significant advancements have been achieved in each individual module of this pipeline, limited attention is received to improve the pipeline itself. In this talk, I will introduce an alternative to this standard pipeline, which first forecasts LiDAR point clouds. Then, detection and tracking are performed on the predicted point clouds to obtain future object trajectories. As forecasting LiDAR point clouds does not require object labels for training, we can scale performance with more unlabeled data.

To deal with the challenge in point cloud forecasting, I will also talk about a few techniques that can produce point cloud sequence with significantly more fine-grained details. Finally, as an emerging task in autonomous driving, I will talk about a new perception dataset we have built to benchmark the point cloud forecasting task. This new dataset is all-inclusive in terms of sensor modalities, annotations and environmental variations. We hope that this dataset can help benchmark progress in point cloud forecasting and innovate multi-sensor multi-task perception systems.