论文标题

审查全景成像及其在场景理解中的应用

Review on Panoramic Imaging and Its Applications in Scene Understanding

论文作者

Gao, Shaohua, Yang, Kailun, Shi, Hao, Wang, Kaiwei, Bai, Jian

论文摘要

随着高速通信和人工智能技术的快速发展,人类对现实场景的看法不再限于使用小型视野(FOV)和低维场景检测设备。全景成像成为下一代环境感知和测量的创新智能工具。然而,尽管满足了对大型摄影成像的需求,但全景成像仪器预计将具有高分辨率,没有盲目的区域,微型化和多维智能感知,并且可以与人工智能方法结合到下一代智力仪器方面,使人对360度的现实环境的深入理解和更全面的环境环境周围环境,使得更深入地了解360度的环境。幸运的是,自由形式表面,薄板光学和metasurfaces的最新进展提供了解决人类对环境感知的创新方法,从而超出了传统的光学成像以外的有希望的想法。在这篇评论中,我们首先介绍全景成像系统的基本原理,然后描述各种全景成像系统的体系结构,功能和功能。之后,我们详细讨论了全景成像中自由形式表面,薄板光学和元信息的广泛应用前景和巨大的设计潜力。然后,我们提供了有关这些技术如何帮助增强全景成像系统性能的详细分析。我们进一步提供了全景成像在场景理解中的应用,用于自动驾驶和机器人技术的应用,跨越全景语义图像分割,全景深度估计,全景视觉定位等等。最后,我们对全景成像工具的未来潜力和研究方向提出了看法。

With the rapid development of high-speed communication and artificial intelligence technologies, human perception of real-world scenes is no longer limited to the use of small Field of View (FoV) and low-dimensional scene detection devices. Panoramic imaging emerges as the next generation of innovative intelligent instruments for environmental perception and measurement. However, while satisfying the need for large-FoV photographic imaging, panoramic imaging instruments are expected to have high resolution, no blind area, miniaturization, and multidimensional intelligent perception, and can be combined with artificial intelligence methods towards the next generation of intelligent instruments, enabling deeper understanding and more holistic perception of 360-degree real-world surrounding environments. Fortunately, recent advances in freeform surfaces, thin-plate optics, and metasurfaces provide innovative approaches to address human perception of the environment, offering promising ideas beyond conventional optical imaging. In this review, we begin with introducing the basic principles of panoramic imaging systems, and then describe the architectures, features, and functions of various panoramic imaging systems. Afterwards, we discuss in detail the broad application prospects and great design potential of freeform surfaces, thin-plate optics, and metasurfaces in panoramic imaging. We then provide a detailed analysis on how these techniques can help enhance the performance of panoramic imaging systems. We further offer a detailed analysis of applications of panoramic imaging in scene understanding for autonomous driving and robotics, spanning panoramic semantic image segmentation, panoramic depth estimation, panoramic visual localization, and so on. Finally, we cast a perspective on future potential and research directions for panoramic imaging instruments.

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