Abstract We present Hybrid Voxel Network (HVNet), a novel one-stage unified network for
point cloud based 3D object detection for autonomous driving. Recent studies show that 2D …

Robust and accurate scene representation is essential for advanced driver assistance
systems (ADAS) such as automated driving. The radar and camera are two widely used …

In recent years 3D object detection from LiDAR point clouds has made great progress
thanks to the development of deep learning technologies. Although voxel or point based …

There have been significant advances in neural networks for both 3D object detection using
LiDAR and 2D object detection using video. However, it has been surprisingly difficult to …

Accurate and reliable perception systems are essential for autonomous driving and robotics.
To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors …

Point cloud 3D object detection has recently received major attention and becomes an
active research topic in 3D computer vision community. However, recognizing 3D objects in …

LiDAR-based 3D object detection pushes forward an immense influence on autonomous
vehicles. Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected …

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving.
Existing one-stage 3D object detection methods can achieve real-time performance …

LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of
both academia and industry since it relies upon a sensor that incorporates appealing …

Integrating LiDAR and Camera information into Bird's-Eye-View (BEV) has become an
essential topic for 3D object detection in autonomous driving. Existing methods mostly adopt …