LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …

Densely annotating LiDAR point clouds is costly, which often restrains the scalability of fully-
supervised learning methods. In this work, we study the underexplored semi-supervised …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key
regions, such as corners, are not directly graspable, making manipulation difficult. We …

While numerous 3D detection works leverage the complementary relationship between RGB
images and point clouds, developments in the broader framework of semi-supervised object …

Pseudo-Labeling (PL) is a critical approach in semi-supervised 3D object detection (SSOD).
In PL, delicately selected pseudo-labels, generated by the teacher model, are provided for …

Semi-supervised 3D object detection is a promising yet under-explored direction to reduce
data annotation costs, especially for cluttered indoor scenes. A few prior works, such as …

Semi-supervised learning aims to leverage numerous unlabeled data to improve the model
performance. Current semi-supervised 3D object detection methods typically use a teacher …

Three dimensional (3D) object detection with an optical camera and light detection and
ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The …