Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Monocular 3D object detection is an important yet challenging task in autonomous driving.
Some existing methods leverage depth information from an off-the-shelf depth estimator to …

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 aims to localize 3D bounding boxes in an input single 2D
image. It is a highly challenging problem and remains open, especially when no extra …

In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-
modality and single-modality is diverse and complex. Since the multi-modal methods have …

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

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by
enhancing the capability of perceiving depth with depth estimation networks, and using …

LiDAR and cameras are complementary sensors for 3D object detection in autonomous
driving. However, it is challenging to explore the unnatural interaction between point clouds …

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 …