Vision-based 3D Detection task is fundamental task for the perception of an autonomous driving system, which has peaked interest amongst many researchers and autonomous driving engineers. However achieving a rather good 3D BEV (Bird's Eye View) performance is not an easy task using 2D sensor input-data with cameras. In this paper we provide a literature survey for the existing Vision Based 3D detection methods, focused on autonomous driving. We have made detailed analysis of over $60$ papers leveraging Vision BEV detections approaches and highlighted different sub-groups for detailed understanding of common trends. Moreover, we have highlighted how the literature and industry trend have moved towards surround-view image based methods and note down thoughts on what special cases this method addresses. In conclusion, we provoke thoughts of 3D Vision techniques for future research based on shortcomings of the current techniques including the direction of collaborative perception.
翻译:基于愿景的3D探测任务,是认识自主驾驶系统的根本任务,它使许多研究人员和自主驾驶工程师的兴趣达到顶峰。然而,实现相当好的3D BEV(Bird's Eye View)性能并非一项容易的任务,使用摄像头使用2D传感器输入数据。在本文中,我们对现有的基于愿景的3D探测方法进行文献调查,重点是自主驾驶。我们详细分析了60多美元的利用愿景BEV探测方法的文件,并强调了不同分组,以详细了解共同趋势。此外,我们强调了文学和行业趋势如何转向以环视图像为基础的方法,并记下了关于这一方法所处理的特殊案例的想法。最后,我们根据当前技术的缺陷,包括协作认知方向,为未来研究提出了三D愿景技术的想法。</s>