Abstract: Vision-based object detection is an important component of environment perception systems. It has been a research hotspot in computer vision, robotics and other related fields. The 3D object detection is based on the 2D object detection, which involves the estimation of the object scale, localization and pose estimation in the camera coordinate. Compared to 2D object detection, there are still a big gap for 3D object detection in terms of accuracy and real-time performance. This paper systematically surveys the state-of-the-art vision-based 3D object detection methods based on monocular vision, stereo vision and RGB-D, and classifies them according to indoor and outdoor scenes. In addition, the paper compares and analyzes these methods on KITTI, SUN RGB-D and other datasets, and discusses on the future research direction.

Key words: computer-vision, 3D object detection, indoor scene, outdoor scene, monocular vision, stereo/depth vision

摘要： 基于视觉的目标检测是环境感知系统的重要组成，一直以来是计算机视觉、机器人等相关领域的研究热点。三维目标检测是在二维目标检测的基础上，增加目标尺寸、深度、姿态等信息的估计。相比于二维目标检测，三维目标检测在准确性、实时性等方面仍有较大的提升空间。系统总结了基于视觉的三维目标检测方法，调研了现有的基于单目视觉、双目、深度相机的三维目标检测方法，并依据室内外场景进行了分类。此外，在KITTI、SUN RGB-D等数据集上对最新的三维目标检测算法进行了对比分析，并针对目前算法中存在的难点和问题，讨论了未来的研究方向。

关键词: 计算机视觉, 三维目标检测, 室内场景, 室外场景, 单目视觉, 双目/深度视觉