3D object detection methods based on camera and LiDAR fusion are susceptible to
environmental noise. Due to the mismatch of physical characteristics of the two sensors, the …

In this paper, we propose a 3D object detection method called MMAF-Net that is based on
the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data …

Autonomous Vehicles (AVs) are expected to be intelligent enough to perceive the world
accurately in terms of avoiding road obstacles. Remarkable progress has been made in 3D …

Self-driving cars must detect vehicles, pedestrians, and other traffic participants accurately to
operate safely. Small, far-away, or highly occluded objects are particularly challenging …

To our knowledge, the fastest 3D object detector on LiDAR data works at 42.03 point clouds-
per-second on high-end machines and 6.15 point clouds-per-second on embedded boards …

Road detection is one of the most basic tasks of autonomous driving systems. At present,
researches on this issue mainly take two kinds of data as input, ie, LIDAR point clouds and …

3D visual recognition is a prerequisite for most autonomous robotic systems operating in the
real world. It empowers robots to perform a variety of tasks, such as tracking, understanding …

In order for autonomous vehicles to safely navigate the road ways, accurate object detection
must take place before safe path planning can occur. Currently, general purpose object …

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

Pixel image data of a scene is received in which the pixel image data includes a two-
dimensional representation of an object in the scene. Point cloud data including three …