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

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 …

Aiming at accurate and real-time object detection on 3D point clouds, we proposed a single-
stage deep neural network which includes new solutions in three aspects: network …

The detection of small objects such as pedestrians still poses challenges to the LiDAR-
based 3D object detection due to the sparseness and disorder of point clouds. Conversely …

LiDAR and camera are two essential sensors for 3D object detection in autonomous driving.
LiDAR provides accurate and reliable 3D geometry information while the camera provides …

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 …

Abstract 3D object detection is a key element for the perception of autonomous vehicles.
LiDAR sensors are commonly used to perceive the surrounding area, producing a sparse …

RGB cameras and LiDAR are crucial sensors for autonomous vehicles that provide
complementary information for accurate detection. Recent early-level fusion-based …

Camera and LIDAR are both important sensor modalities for real-world applications,
especially autonomous driving. The sensors provide complementary information and make …

In this paper, we present a new deep neural network architecture, which detects objects in
bird's eye view (BEV) using Lidar sensor data in autonomous driving scenarios. The key …