论文标题
RUHSNET：实时使用LIDAR数据的3D对象检测
RUHSNet: 3D Object Detection Using Lidar Data in Real Time
论文作者
论文摘要
在这项工作中，我们实时解决了点云数据的3D对象检测问题。对于自动驾驶汽车的工作，对于感知组成部分而言，以高精度和快速推理来检测现实世界对象非常重要。我们提出了一种新颖的神经网络体系结构，以及用于检测点云数据中3D对象的培训和优化细节。我们将结果与不同的骨干结构进行比较，包括标准的架构，例如VGG，Resnet，Inception和我们的骨干。我们还提供了优化和消融研究，包括设计有效的锚点。我们使用Kitti 3D Birds眼视图数据集进行基准测试和验证我们的结果。我们的工作在平均精度和速度> 30 fps的情况下都超过了该领域的最新技术。这使得它是一个可行的选择，可以在包括自动驾驶汽车在内的实时应用程序中部署。
In this work, we address the problem of 3D object detection from point cloud data in real time. For autonomous vehicles to work, it is very important for the perception component to detect the real world objects with both high accuracy and fast inference. We propose a novel neural network architecture along with the training and optimization details for detecting 3D objects in point cloud data. We compare the results with different backbone architectures including the standard ones like VGG, ResNet, Inception with our backbone. Also we present the optimization and ablation studies including designing an efficient anchor. We use the Kitti 3D Birds Eye View dataset for benchmarking and validating our results. Our work surpasses the state of the art in this domain both in terms of average precision and speed running at > 30 FPS. This makes it a feasible option to be deployed in real time applications including self driving cars.
