The 3D object detection is becoming indispensable for environmental perception in
autonomous driving. Light detection and ranging (LiDAR) point clouds often fail to …

There have been significant advances in 3D object detection using LiDAR and camera
fusion for autonomous driving. However, it is surprisingly difficult to effectively design fusion …

Camera-LiDAR 3D object detection has been extensively investigated due to its significance
for many real-world applications. However, there are still of great challenges to address the …

Currently, many kinds of LiDAR-camera-based 3D object detectors have been developed
with two heavy neural networks to extract view-specific features, while a LiDAR-camera …

In this paper, we propose a 3D object detection method called MMAF-Net that is based on
the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data …

Three-dimensional object detection plays a key role in autonomous driving systems. The
performance of light detection and ranging (LiDAR)-based models is limited because of the …

As a key task in autonomous driving, 3D object detection based on LiDAR-camera fusion is
expected to achieve more robust results by the complementarity of the two sensors …

Today, multi-sensor fusion detection frameworks in autonomous driving, especially
sequence-based data-level fusion frameworks, face high latency and coupling issues and …

Lidars and cameras are critical sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, accurate and robust fusion …

Although LiDAR sensors are crucial for autonomous systems due to providing precise depth
information, they struggle with capturing fine object details, especially at a distance, due to …