Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

3D object detection plays a key role in the perception system of intelligent vehicles. The
reliable 3D structural information provided by LiDAR points enables the accurate regression …

Existing data augmentation approaches on LiDAR point cloud are mostly developed on rigid
transformation, such as rotation, flipping, or copy-based and mix-based methods, lacking the …

3D object detection is a critical task in the field of robotics and autonomous driving. Although
recent LiDAR-based 3D object detection techniques have been well-studied and achieve …

Long-range target geolocalization in outdoor complex environments has been a long-term
challenge in intelligent transportation and autonomous vehicles with great interest in fields …

With the remarkable achievements of stereo matching algorithms based on Convolutional
neural Networks (CNNs), more and more stereo matching algorithms based on CNNs are …

The provision of real-time, accurate perception of vulnerable road users (VRUs) via
infrastructure-sensors-based devices is integral to roadside perception in vehicle …

Existing 3D object detection suffers from expensive annotation costs and poor transferability
to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to …

Three-dimensional object detection algorithms are becoming increasingly crucial in
autonomous driving, demanding high accuracy, and reasoning speed. Due to the uneven …

Vision-based object detection for intelligent vehicles differs from regular object detection
tasks. It necessitates an attention region where objects have a critical impact on the vehicle's …