Real-time 3D Object Detection using Feature Map Flow

Murhij, Youshaa; Yudin, Dmitry

doi:10.48550/arxiv.2106.14101

Cited by 1 publication

(1 citation statement)

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“…In [48], object proposals are generated based on temporal occupancy maps, odometry data, and Kalman filter-based motion estimations. [49] aggregates two consecutive feature maps using odometry-based flow estimation. [50] combines point clouds from multiple frames by tagging the points with timestamps, which are aligned using the ego-motion compensation and used as the input.…”

Section: Challenges and Open Fieldsmentioning

confidence: 99%

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

Zimmer¹,

Erçelik²,

Zhou³

et al. 2022

Preprint

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The purpose of this work is to review the stateof-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. I. INTRODUCTIONAutonomous driving (AD) is increasingly gaining attention worldwide and can lead to many advantages to the population. The potential of this technology is clear, and it is predicted that it will dramatically change the transportation sector. The application of robust 3D Object Detection in autonomous driving is vital. In this context, robustness means to cope with detection errors (e.g., occlusions) and erroneous input (e.g., sensor noise). Sometimes, the environment is not optimal for detecting objects (e.g., in rain, snow, fog or bright sunlight). Robust detection stands for a good generalization to detect unseen objects in a different environment.Besides, infrastructure sensors (like the ones in the Providentia [1] system) support the perception of the environment, improve traffic safety, and offer higher robustness and performance through different mounting positions. Multiple view points help to better detect objects in 3D (position, orientation, and size), and to increase the robustness against sunlight, snow, dust, and fog.The aim of this survey paper is to provide an overview of novel 3D object detection methods and tricks. The best LiDAR-based 3D object detection algorithm on the KITTI dataset is 68.63% more accurate (on the 3D car class) than the best camera-only 3D object detection algorithm on that dataset. At night, the LiDAR performs better and is able to detect objects within a range of 50-60 m (Ouster OS1-64 gen. 2 LiDAR).Our contribution is partitioned into the following:• We provide state-of-the-art LiDAR-based 3D object detectors and show their accuracy, speed, and robustness.

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Section: Challenges and Open Fieldsmentioning

confidence: 99%