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

The past decade has witnessed the rapid development of autonomous driving systems.
However, it remains a daunting task to achieve full autonomy, especially when it comes to …

Recent advances in cross-modal 3D object detection rely heavily on anchor-based methods,
and however, intractable anchor parameter tuning and computationally expensive …

Multi-modal 3D object detection has been an active research topic in autonomous driving.
Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D …

In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities
with complementary cues for 3D object detection. However, it is quite difficult to sufficiently …

3D object detection is a common function within the perception system of an autonomous
vehicle and outputs a list of 3D bounding boxes around objects of interest. Various 3D object …

How to ensure robust and accurate 3D object detection under various environment is
essential for autonomous driving (AD) environment perception. While, until now, most of the …

With the development of society, technological progress, and new needs, autonomous
driving has become a trendy topic in smart cities. Due to technological limitations …

Object detection through either RGB images or the LiDAR point clouds has been extensively
explored in autonomous driving. However, it remains challenging to make these two data …

Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …