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 …

The transportation detection of long-distance small objects has low accuracy. In this work,
we propose DMF, which is based on disparity depths. We map different disparity regions to …

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 …

Despite recent stereo matching networks achieving impressive performance given sufficient
training data, they suffer from domain shifts and generalize poorly to unseen domains. We …

Feature matching is an important computer vision task that involves estimating
correspondences between two images of a 3D scene and dense methods estimate all such …

3D object detection from images, one of the fundamental and challenging problems in
autonomous driving, has received increasing attention from both industry and academia in …

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 …

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 …