LiDAR-based scene flow estimation (SFE) and moving object segmentation (MOS) are
important tasks with broad-ranging applications in autonomous driving, such as traffic …

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 …

Camera and LIDAR are both important sensor modalities for real-world applications,
especially autonomous driving. The sensors provide complementary information and make …

LiDAR point cloud streams are usually sparse in time dimension, which is limited by
hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to …

As one of the important tasks in the field of robotics and machine vision, visual odometry
provides tremendous help for various applications such as navigation, location, and so on …

Estimating the depth of a construction scene from a single red‐green‐blue image is a crucial
prerequisite for various applications, including work zone safety, localization, productivity …

Depth completion is the task of reconstructing dense depth images from sparse LiDAR data.
LiDAR depth completion, for which LiDAR data is the only input, is an ill-posed and …

Simultaneous Location and Map** (SLAM) is a common algorithm for position estimation
in GNSS-denied environments. However, the high structural consistency and low lighting …

Electrocution accidents caused by operation and maintenance personnel and high-voltage
live equipment frequently occur in substations. Although many cameras have been installed …

Point cloud frame interpolation is a challenging task that involves accurate scene flow
estimation across frames and maintaining the geometry structure. Prevailing techniques …