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 …

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 …

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 …

Autonomous driving is regarded as one of the most promising remedies to shield human
beings from severe crashes. To this end, 3D object detection serves as the core basis of …

Abstract 3D object detection is receiving increasing attention from both industry and
academia thanks to its wide applications in various fields. In this paper, we propose Point …

Locating 3D objects from a single RGB image via Perspective-n-Points (PnP) is a long-
standing problem in computer vision. Driven by end-to-end deep learning, recent studies …

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 …

There have been significant advances in neural networks for both 3D object detection using
LiDAR and 2D object detection using video. However, it has been surprisingly difficult 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 …