论文标题
调查和系统化3D对象检测模型和方法
Survey and Systematization of 3D Object Detection Models and Methods
论文作者
论文摘要
对自动驾驶汽车的强劲需求和3D传感器的广泛可用性正在不断加油3D对象检测的新方法。在本文中，我们对2012年至2021年的3D对象检测的最新发展进行了全面调查，其中涵盖了输入数据的完整管道，数据表示和特征提取到实际检测模块。我们介绍了基本的概念，专注于过去十年中出现的广泛的不同方法，并提出了一个系统化，该系统化提供了一个实用的框架，可以将这些方法与指导未来发展，评估和应用活动的目标进行比较。具体而言，我们对3D对象检测模型和方法的调查和系统化可以帮助研究人员和从业人员通过将3DOD解决方案分解为更易于管理的部分来快速概述该领域。
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.
