Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Autonomous driving requires an accurate and fast 3D perception system that includes 3D
object detection, tracking, and segmentation. Although recent low-cost camera-based …

Occluded and long-range objects are ubiquitous and challenging for 3D object detection.
Point cloud sequence data provide unique opportunities to improve such cases, as an …

Existing data augmentation approaches on LiDAR point cloud are mostly developed on rigid
transformation, such as rotation, flip**, or copy-based and mix-based methods, lacking the …

This study is a systematic review that intends to improve our knowledge of the ideas of open
data synchronization and standardization by reviewing the methodology and instruments …

Accurate and robust 3D object detection is a critical component in autonomous vehicles and
robotics. While recent radar-camera fusion methods have made significant progress by …

Tracking objects in three-dimensional space is critical for autonomous driving. To ensure
safety while driving, the tracker must be able to reliably track objects across frames and …

3D object detectors for point clouds often rely on a pooling-based PointNet to encode sparse
points into grid-like voxels or pillars. In this paper, we identify that the common PointNet …

In the supplementary material, we provide additional details on our evaluation setting,
include additional analysis, and finally note limitations. In Sec. 1, we provide additional …