L Liu, J He, K Ren, Z Xiao, Y Hou - Information, 2022 - mdpi.com
3D object detection with LiDAR and camera fusion has always been a challenge for autonomous driving. This work proposes a deep neural network (namely FuDNN) for LiDAR …
R Guo, D Li, Y Han - Pattern Recognition Letters, 2021 - Elsevier
The perception of 3D objects in the scene is the basis of autonomous driving. Most autonomous driving cars are equipped with cameras and Lidar to obtain 3D spatial …
Camera and LIDAR are both important sensor modalities for real-world applications, especially autonomous driving. The sensors provide complementary information and make …
Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor …
X Zhang, L Wang, G Zhang, T Lan… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
The 3D object detection is becoming indispensable for environmental perception in autonomous driving. Light detection and ranging (LiDAR) point clouds often fail to …
JH Yoo, Y Kim, J Kim, JW Choi - … conference, Glasgow, UK, August 23–28 …, 2020 - Springer
In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for 3D object detection. Because the camera and LiDAR sensor signals have different …
3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse …
This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection …
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB …