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 is an inherently ill-posed problem, as it is challenging to
predict accurate 3D localization from a single image. Existing monocular 3D detection …

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D
detection has brought significant improvement, eg, Pseudo-LiDAR methods. However, the …

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 …

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 …

We propose a novel semi-supervised active learning framework for monocular 3D object
detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data …

Monocular 3D object detection is a low-cost but challenging task, as it requires generating
accurate 3D localization solely from a single image input. Recent developed depth-assisted …

Compared to typical multi-sensor systems, monocular 3D object detection has attracted
much attention due to its simple configuration. However, there is still a significant gap …

Monocular 3D object detection usually adopts direct or hierarchical label supervision.
Recently, the distillation supervision transfers the spatial knowledge from LiDAR-or stereo …

Monocular 3D object detection is challenging due to the lack of accurate depth information.
Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and …