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

Monocular 3D object detection poses a significant challenge due to the lack of depth
information in RGB images. Many existing methods strive to enhance the object depth …

Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in
autonomous driving due to absence of explicit depth cues in a single RGB image. In this …

Monocular 3D object detection is a promising yet ill-posed task for autonomous vehicles due
to the lack of accurate depth information. Cross-modality knowledge distillation could …

Monocular 3D object detection (M3OD) is a significant yet inherently challenging task in
autonomous driving due to absence of implicit depth cues in a single RGB image. In this …

Real-time processing is crucial in autonomous driving systems due to the imperative of
instantaneous decision-making and rapid response. In real-world scenarios, autonomous …

LiDAR point cloud segmentation is one of the most fundamental tasks for autonomous
driving scene understanding. However, it is difficult for existing models to achieve both high …

Monocular 3D object detection (Mono3D) is an indispensable research topic in autonomous
driving, thanks to the cost-effective monocular camera sensors and its wide range of …

The use of knowledge distillation in monocular 3D object detection has been explored by
incorporating a LiDAR model as the teacher network to transfer knowledge to a monocular …

Monocular 3D object detection aims for precise 3D localization and identification of objects
from a single-view image. Despite its recent progress, it often struggles while handling …