The wide deployment of multi-modal sensors in various areas generates vast amounts of
data with characteristics of high volume, wide variety, and high integrity. However, traditional …

3D-LiDAR-based cooperative perception has been generating significant interest for its
ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues …

Change detection is an important step for the characterization of object dynamics at the
earth's surface. In multi-temporal point clouds, the main challenge is to detect true changes …

Estimating the 3D position and orientation of objects in the environment with a single RGB
camera is a critical and challenging task for low-cost urban autonomous driving and mobile …

Abstract 3D scene flow estimation from point clouds is a low-level 3D motion perception task
in computer vision. Flow embedding is a commonly used technique in scene flow estimation …

Semantic segmentation of urban scenes is an essential component in various applications
of autonomous driving. It makes great progress with the rise of deep learning technologies …

Abstract 3D object tracking in point clouds is still a challenging problem due to the sparsity of
LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV …

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential
in a commercial application. It aims to predict multiple pedestrian trajectories and maintain …

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits
feature fusion from both modalities for point-wise scene flow estimation. Previous methods …

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 …