A Survey of Robust 3D Object Detection Methods in Point Clouds

• URL: http://arxiv.org/abs/2204.00106v1
• Date: Thu, 31 Mar 2022 21:41:32 GMT
• Title: A Survey of Robust 3D Object Detection Methods in Point Clouds
• Authors: Walter Zimmer, Emec Ercelik, Xingcheng Zhou, Xavier Jair Diaz Ortiz and Alois Knoll
• Abstract summary: We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. We evaluate novel 3D object detectors on the KITTI, nuScenes, and dataset. We mention the current challenges in 3D object detection in LiDAR point clouds and list some open issues.
• Score: 2.1655448059430222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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