Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

Understanding how the surrounding environment changes is crucial for performing
downstream tasks safely and reliably in autonomous driving applications. Recent occupancy …

Aiming at obtaining structural information and 3D motion of dynamic scenes, scene flow
estimation has been an interest of research in computer vision and computer graphics for a …

LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving.
Due to the motion of objects and static/dynamic occlusion, temporal information plays an …

Point cloud data has been extensively studied due to its compact form and flexibility in
representing complex 3D structures. The ability of point cloud data to accurately capture and …

In recent years, there has been a significant increase in focus on the interpolation task of
computer vision. Despite the tremendous advancement of video interpolation, point cloud …

In this work, we propose a new sequential point cloud upsampling method called SPU,
which aims to upsample sparse, non-uniform, and orderless point cloud sequences by …

The seepage field of tailings dam is closely related to the safety state. Real-time evaluation
of seepage field safety based on monitoring data is of great significance to ensure the safe …

The ability to predict future structure features of environments based on past perception
information is extremely needed by autonomous vehicles, which helps to make the following …

Pose prediction and trajectory forecasting represent pivotal tasks in the realm of
autonomous driving, crucially enhancing the planning and decision-making capabilities of …