Leveraging Temporal Information for 3D Detection and Domain Adaptation
This work addresses a gap in autonomous driving perception by enhancing detection accuracy, though it appears incremental as it builds on existing methods with a simple modification.
The paper tackled the problem of 3D object detection in autonomous driving by incorporating temporal information into point cloud learning, resulting in consistent improvements across all three object classes.
Ever since the prevalent use of the LiDARs in autonomous driving, tremendous improvements have been made to the learning on the point clouds. However, recent progress largely focuses on detecting objects in a single 360-degree sweep, without extensively exploring the temporal information. In this report, we describe a simple way to pass such information in the learning pipeline by adding timestamps to the point clouds, which shows consistent improvements across all three classes.