Strong demand for autonomous vehicles and the wide availability of 3D sensors are continuously fueling the proposal of novel methods for 3D object detection. In this paper, we provide a comprehensive survey of recent developments from 2012-2021 in 3D object detection covering the full pipeline from input data, over data representation and feature extraction to the actual detection modules. We introduce fundamental concepts, focus on a broad range of different approaches that have emerged over the past decade, and propose a systematization that provides a practical framework for comparing these approaches with the goal of guiding future development, evaluation and application activities. Specifically, our survey and systematization of 3D object detection models and methods can help researchers and practitioners to get a quick overview of the field by decomposing 3DOD solutions into more manageable pieces.

翻译：自动驾驶车辆的强烈需求以及3D传感器的广泛可用性，持续推动着三维目标检测新方法的提出。本文对2012至2021年间三维目标检测领域的最新发展进行了全面综述，覆盖从输入数据、数据表示与特征提取，到实际检测模块的完整流程。我们介绍了基本概念，聚焦于过去十年间涌现的多种不同方法，并提出了一种系统化分类框架，旨在为比较这些方法提供实用框架，以指导未来的开发、评估与应用活动。具体而言，本综述与系统化工作通过将三维目标检测解决方案分解为更易管理的模块，能够帮助研究人员和实践者快速把握该领域全貌。