论文标题
自动驾驶图像中的3D对象检测：调查
3D Object Detection from Images for Autonomous Driving: A Survey
论文作者
论文摘要
近年来，来自图像的3D对象检测是自动驾驶中的基本和挑战性问题之一，近年来受到了行业和学术界的越来越多的关注。从深度学习技术的快速发展中受益，基于图像的3D检测取得了显着的进步。特别是，从2015年到2021年，有200多种工作研究了这个问题，其中包括广泛的理论，算法和应用程序。但是，迄今为止，最近还没有进行收集和组织这些知识的调查。在本文中，我们在文献中填补了这一空白，并对这个小说且不断增长的研究领域进行了首次全面调查，总结了用于基于图像的3D检测的最常用的管道，并深入分析了它们的每个组件。此外，我们还提出了两种新的分类法，将最新方法组织为不同类别，目的是对现有方法进行更系统的审查，并促进与未来工作的公平比较。回顾迄今为止取得的成就，我们还分析了该领域的当前挑战，并讨论了基于图像的3D检测研究的未来方向。
3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years. Benefiting from the rapid development of deep learning technologies, image-based 3D detection has achieved remarkable progress. Particularly, more than 200 works have studied this problem from 2015 to 2021, encompassing a broad spectrum of theories, algorithms, and applications. However, to date no recent survey exists to collect and organize this knowledge. In this paper, we fill this gap in the literature and provide the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection and deeply analyzing each of their components. Additionally, we also propose two new taxonomies to organize the state-of-the-art methods into different categories, with the intent of providing a more systematic review of existing methods and facilitating fair comparisons with future works. In retrospect of what has been achieved so far, we also analyze the current challenges in the field and discuss future directions for image-based 3D detection research.
