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 …

Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …

摘要视觉目标检测旨在定位和识别图像中存在的物体, 属于计算机视觉领域的经典任务之一,
也是许多计算机视觉任务的前提与基础, 在自动驾驶, 视频监控等领域具有重要的应用价值 …

It has been well recognized that fusing the complementary information from depth-aware
LiDAR point clouds and semantic-rich stereo images would benefit 3D object detection …

Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images
using intermediate depth maps or implicit 3D geometry representations, which provides a …

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 …

An accurate and robust perception system is key to understanding the driving environment
of autonomous driving and robots. Autonomous driving needs 3-D information about objects …

Camera-based 3D object detectors are welcome due to their wider deployment and lower
price than LiDAR sensors. We first revisit the prior stereo detector DSGN for its stereo …

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

Many LiDAR-based methods for detecting large objects, single-class object detection, or
under easy situations were claimed to perform well. However, due to their failure to exploit …