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 …

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 …

3D object detection is a fundamental and challenging task for 3D scene understanding, and
the monocular-based methods can serve as an economical alternative to the stereo-based …

Monocular 3D detection has drawn much attention from the community due to its low cost
and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space …

As a crucial task of autonomous driving, 3D object detection has made great progress in
recent years. However, monocular 3D object detection remains a challenging problem due …

As an inherently ill-posed problem, depth estimation from single images is the most
challenging part of monocular 3D object detection (M3OD). Many existing methods rely on …

Monocular 3D object detection has seen significant advancements with the incorporation of
depth information. However, there remains a considerable performance gap compared to …

Monocular 3D object detection has recently become prevalent in autonomous driving and
navigation applications due to its cost-efficiency and easy-to-embed to existent vehicles. The …

In the context of autonomous driving, it is both critical and challenging to locate 3D objects
by using a calibrated RGB image. Current methods typically utilize heteroscedastic aleatoric …