Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

As the foundation of intelligent systems, machine vision perceives the surrounding
environment and provides a basis for decision-making. Object detection is the core task in …

Recent voxel-based 3D object detectors for autonomous vehicles learn point cloud
representations either from bird eye view (BEV) or range view (RV, aka the perspective …

High-efficiency point cloud 3D object detection operated on embedded systems is important
for many robotics applications including autonomous driving. Most previous works try to …

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 …

环境感知与地下空间导航是煤矿智能化信息领域的重要研究方向, 对实现无人化, 全自动化,
智能化的煤矿生产作业至关重要. 随着第五代移动通信技术(5th generation mobile networks …

Current neural networks-based object detection approaches processing LiDAR point clouds
are generally trained from one kind of LiDAR sensors. However, their performances …

Object detection algorithms for Lidar data have seen numerous publications in recent years,
reporting good results on dataset benchmarks oriented towards automotive requirements …

To endow machines with the ability to perceive the real-world in a three dimensional
representation as we do as humans is a fundamental and long-standing topic in Artificial …

オブジェクト検出は 2012 年から Deep Learning 時代に入り, 勾配ベース手法から Convolutional
Neural Network (CNN) を利用した手法に大きく移っている. オブジェクト検出が 2D …