Single object tracking (SOT) in light detection and ranging (LiDAR) point clouds is a
challenging problem in computer vision. Compared to object-level point clouds, scene-level …

Abstract 3D single object tracking has been a crucial problem for decades with numerous
applications such as autonomous driving. Despite its wide-ranging use, this task remains …

Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese
tracking paradigm, which first generates proposals and then predicts corresponding …

3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential
for applications like autonomous driving. Sparse and occluded data in scene point clouds …

The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various
applications, such as autonomous driving and robotics. However, existing approaches have …

3D single object tracking plays a crucial role in numerous applications such as autonomous
driving. Recent trackers based on motion-centric paradigm perform well as they exploit …

3D single object tracking (SOT) methods based on appearance matching has long suffered
from insufficient appearance information incurred by incomplete, textureless and …

3D single object tracking remains a challenging problem due to the sparsity and
incompleteness of the point clouds. Existing algorithms attempt to address the challenges in …

The point cloud-based 3D single object tracking plays an indispensable role in autonomous
driving. However, the application of 3D object tracking in the real world is still challenging …

Siamese trackers based on 3D region proposal network (RPN) have shown remarkable
success with deep Hough voting. However, using a single seed point feature as the cue for …