论文标题
以雷达为中心的3D对象检测深度学习
Deep Learning on Radar Centric 3D Object Detection
论文作者
论文摘要
即使许多现有的3D对象检测算法主要依赖相机和激光镜头，但相机和激光镜头容易受到恶劣的天气和照明条件的影响。另一方面，雷达对这种条件具有抵抗力。但是，研究直到最近才发现对雷达数据应用深度神经网络。在本文中，我们仅使用雷达引入了一种深度学习方法来检测3D对象。据我们所知，我们是第一个以雷达为基础的深度学习3D对象检测模型的人，仅在公共雷达数据集中训练。为了克服缺乏标有数据的雷达数据，我们提出了一种新颖的方法，可以通过将其转换为雷达样点云数据和侵略性雷达增强技术来利用丰富的激光雷达数据。
Even though many existing 3D object detection algorithms rely mostly on camera and LiDAR, camera and LiDAR are prone to be affected by harsh weather and lighting conditions. On the other hand, radar is resistant to such conditions. However, research has found only recently to apply deep neural networks on radar data. In this paper, we introduce a deep learning approach to 3D object detection with radar only. To the best of our knowledge, we are the first ones to demonstrate a deep learning-based 3D object detection model with radar only that was trained on the public radar dataset. To overcome the lack of radar labeled data, we propose a novel way of making use of abundant LiDAR data by transforming it into radar-like point cloud data and aggressive radar augmentation techniques.
