论文标题
对点云中强大的3D对象检测方法的调查
A Survey of Robust 3D Object Detection Methods in Point Clouds
论文作者
论文摘要
这项工作的目的是审查基于最新的激光雷达的3D对象检测方法，数据集和挑战。我们描述了新的数据增强方法，采样策略，激活功能，注意机制和正则化方法。此外，我们列出了最近引入了归一化方法，学习率时间表和损失功能。此外，我们还涵盖了10个新型自动驾驶数据集的优势和局限性。我们评估了Kitti，Nuscenes和Waymo数据集上的新型3D对象探测器，并显示它们的准确性，速度和鲁棒性。最后，我们提到了LiDar Point Clouds中3D对象检测中当前的挑战，并列出了一些开放问题。
The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges. We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. Furthermore, we list recently introduced normalization methods, learning rate schedules and loss functions. Moreover, we also cover advantages and limitations of 10 novel autonomous driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes, and Waymo dataset and show their accuracy, speed, and robustness. Finally, we mention the current challenges in 3D object detection in LiDAR point clouds and list some open issues.
