In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Non-uniformed 3D sparse data, eg, point clouds or voxels in different spatial positions, make
contribution to the task of 3D object detection in different ways. Existing basic components in …

Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding.
However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5 D as they …

Recently, fusing the LiDAR point cloud and camera image to improve the performance and
robustness of 3D object detection has received more and more attention, as these two …

Abstract Autonomous Driving Vehicle (ADV) services have become a prominent motif in
intelligent vehicle technology by adapting deep learning features. Automated driverless …

3D perception is a critical problem in autonomous driving. Recently, the Bird's-Eye-View
(BEV) approach has attracted extensive attention, due to low-cost deployment and desirable …

At present, the LiDAR ground filtering technology is very mature. There are fewer
applications in 3D-object detection due to the limitations of filtering accuracy and efficiency …

With the improvements in sensor performance (cameras, Lidars) and the application of deep
learning in object detection, autonomous vehicles (AVs) are gradually becoming more …

With the astonishing development of 3D sensors, point cloud based 3D object detection is
attracting increasing attention from both industry and academia, and widely applied in …

LiDAR-based sparse 3-D object detection plays a crucial role in autonomous driving
applications due to its computational efficiency advantages. Existing methods either use the …