At present the perception system of autonomous vehicles is grounded on 3D vision
technologies along with deep learning to process depth information. Although deep learning …

3D single object tracking is a key issue for robotics. In this paper, we propose a transformer
module called Point-Track-Transformer (PTT) for point cloud-based 3D single object …

LiDAR-based 3D single object tracking is a challenging issue in robotics and autonomous
driving. Currently, existing approaches usually suffer from the problem that objects at long …

The perception system for robotics and autonomous cars relies on the collaboration among
multiple types of sensors to understand the surrounding environment. LiDAR has shown …

Current 3D single object tracking methods are typically based on VoteNet, a 3D region
proposal network. Despite the success, using a single seed point feature as the cue for offset …

Estimating the states of surrounding traffic participants stays at the core of autonomous
driving. In this paper, we study a novel setting of this problem: model-free single-object …

The point cloud based 3D single object tracking has drawn increasing attention. Although
many breakthroughs have been achieved, we also reveal two severe issues. By extensive …

Recent works on 3D single object tracking treat the task as a target-specific 3D detection
task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it …

In recent years, the popularity of depth sensors and three-dimensional (3D) scanners has
led to the rapid development of 3D point clouds. A transformer is a type of deep neural …

Although the use of a Siamese network is the most popular approach in object tracking, it
creates an undesirable trivial solution and requires a large amount of training data reflecting …