Camera and radar sensors have significant advantages in cost, reliability, and maintenance
compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at …

Monocular 3D object detection plays a pivotal role in autonomous driving, presenting a
formidable challenge by requiring the precise localization of 3D objects within a single …

Robust scene perception is an essential prerequisite to ensure the reliability in ship
autonomous driving. However, it is a challenging task in inland river because of the …

Stereo matching is a prominent research area in autonomous driving and computer vision.
Despite significant progress made by learning-based methods, accurately predicting …

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 (Mono3OD) is a challenging yet cost-effective vision task in
the fields of autonomous driving and mobile robotics. The lack of reliable depth information …

Thanks to the complementary nature of millimeter wave radar and camera, deep learning-
based radar-camera 3D object detection methods may reliably produce accurate detections …

Monocular 3D object detection is a fundamental but very important task to many applications
including autonomous driving, robotic grasping, and augmented reality. Existing leading …

In the wake of developments in consumer electronics, electric vehicles are gradually
becoming representative carriers of next-generation consumer electronics. Electric vehicles …

Autonomous driving perception models are typically composed of multiple functional
modules that interact through complex relationships to accomplish environment …