We propose a new method for fusing LIDAR point cloud and camera-captured images in
deep convolutional neural networks (CNN). The proposed method constructs a new layer …

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 …

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 …

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 …

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 …

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 …

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

We present AVOD, an Aggregate View Object Detection network for autonomous driving
scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB …