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 …

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

In this research, we propose a new 3D object detector with a trustworthy depth estimation,
dubbed BEVDepth, for camera-based Bird's-Eye-View~(BEV) 3D object detection. Our work …

Monocular 3D object detection is an important task for autonomous driving considering its
advantage of low cost. It is much more challenging than conventional 2D cases due to its …

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a
solution with simple configuration compared to typical multi-sensor systems. The main …

Recent progress in 3D object detection from single images leverages monocular depth
estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors …

Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall
into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is …

Abstract 3D object detection in autonomous driving aims to reason “what” and “where” the
objects of interest present in a 3D world. Following the conventional wisdom of previous 2D …

Monocular 3D object detection has received increasing attention due to the wide application
in autonomous driving. Existing works mainly focus on introducing geometry projection to …

The precise localization of 3D objects from a single image without depth information is a
highly challenging problem. Most existing methods adopt the same approach for all objects …