LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

Geometry-based point cloud compression (G-PCC) is a state-of-the-art point cloud
compression standard. While G-PCC achieves excellent performance, its reliance on the …

Assisted and automated driving functions in vehicles exploit sensor data to build situational
awareness, however, the data amount required by these functions might exceed the …

The control interaction in networked marine surface vehicles (NMSVs) mainly involves three
communication channels, ie, vehicle-to-vehicle, sensor-to-controller, and controller-to …

Vision-guided robotic picking in 3-D space is a key technology for industrial automation and
intelligent manufacturing. However, existing methods rely on labeled real-world data for …

LiDAR sensors are widely used in autonomous driving, and the growing storage and
transmission demands have made LiDAR point cloud compression (LPCC) a hot research …

The key to autonomous navigation in unmanned systems is the ability to recognize static
and moving objects in the environment and to support the task of predicting the future state …

3D point clouds have broad applications across various industries and have contributed to
advancements in fields such as autonomous driving, immersive media, metaverse, and …

Point clouds, a prominent method of 3D representation, are extensively utilized across
industries such as autonomous driving, surveying, electricity, architecture, and gaming, and …

Point cloud compression plays a critical role in efficient point cloud storage and
transmission. This paper focuses on the lossy geometric compression of LiDAR point clouds …