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基于双目视觉的仿生复眼全景立体成像

Panoramic Stereo Imaging of a Bionic Compound-Eye Based on Binocular Vision.

作者信息

Wang Xinhua, Li Dayu, Zhang Guang

机构信息

School of Computer Science, Northeast Electric Power University, Jilin 132012, China.

State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

出版信息

Sensors (Basel). 2021 Mar 10;21(6):1944. doi: 10.3390/s21061944.

DOI:10.3390/s21061944
PMID:33802076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7998483/
Abstract

With the rapid development of the virtual reality industry, one of the bottlenecks is the scarcity of video resources. How to capture high-definition panoramic video with depth information and real-time stereo display has become a key technical problem to be solved. In this paper, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed. Combined with the real-time processing algorithm of multi detector mosaic panoramic stereo imaging image, a panoramic stereo real-time imaging system is developed. Firstly, the optical optimization design scheme of panoramic imaging based on binocular stereo vision is proposed, and the space coordinate calibration platform of ultra-high precision panoramic camera based on theodolite angle compensation function is constructed. The projection matrix of adjacent cameras is obtained by solving the imaging principle of binocular stereo vision. Then, a real-time registration algorithm of multi-detector mosaic image and Lucas-Kanade optical flow method based on image segmentation are proposed to realize stereo matching and depth information estimation of panoramic imaging, and the estimation results are analyzed effectively. Experimental results show that the stereo matching time of panoramic imaging is 30 ms, the registration accuracy is 0.1 pixel, the edge information of depth map is clearer, and it can meet the imaging requirements of different lighting conditions.

摘要

随着虚拟现实产业的快速发展,其中一个瓶颈是视频资源的稀缺。如何获取具有深度信息的高清全景视频并进行实时立体显示已成为亟待解决的关键技术问题。本文提出了基于双目立体视觉的全景成像光学优化设计方案。结合多探测器拼接全景立体成像图像的实时处理算法,开发了一种全景立体实时成像系统。首先,提出基于双目立体视觉的全景成像光学优化设计方案,构建基于经纬仪角度补偿功能的超高精度全景相机空间坐标标定平台。通过求解双目立体视觉成像原理得到相邻相机的投影矩阵。然后,提出一种基于图像分割的多探测器拼接图像实时配准算法和Lucas-Kanade光流法,实现全景成像的立体匹配和深度信息估计,并对估计结果进行有效分析。实验结果表明,全景成像的立体匹配时间为30毫秒,配准精度为0.1像素,深度图的边缘信息更清晰,能够满足不同光照条件下的成像要求。

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本文引用的文献

1
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2
Detection of the three-dimensional trajectory of an object based on a curved bionic compound eye.基于弯曲仿生复眼的物体三维轨迹检测
Opt Lett. 2019 Sep 1;44(17):4143-4146. doi: 10.1364/OL.44.004143.
3
Panoramic stereo imaging system for efficient mosaicking: parallax analyses and system design.用于高效拼接的全景立体成像系统:视差分析与系统设计
Appl Opt. 2018 Jan 20;57(3):396-403. doi: 10.1364/AO.57.000396.
4
Scalable Nearest Neighbor Algorithms for High Dimensional Data.高维数据的可扩展最近邻算法。
IEEE Trans Pattern Anal Mach Intell. 2014 Nov;36(11):2227-40. doi: 10.1109/TPAMI.2014.2321376.
5
Improved camera calibration method based on perpendicularity compensation for binocular stereo vision measurement system.基于垂直度补偿的双目立体视觉测量系统改进相机标定方法
Opt Express. 2015 Jun 15;23(12):15205-23. doi: 10.1364/OE.23.015205.
6
Characterization of the AWARE 10 two-gigapixel wide-field-of-view visible imager.AWARE 10两吉像素宽视场可见光成像仪的特性描述。
Appl Opt. 2014 May 1;53(13):C54-63. doi: 10.1364/AO.53.000C54.
7
Development of a scalable image formation pipeline for multiscale gigapixel photography.用于多尺度十亿像素摄影的可扩展图像形成管道的开发。
Opt Express. 2012 Sep 24;20(20):22048-62. doi: 10.1364/OE.20.022048.
8
Multiscale gigapixel photography.多尺度千兆像素摄影。
Nature. 2012 Jun 20;486(7403):386-9. doi: 10.1038/nature11150.
9
Single-camera panoramic stereo imaging system with a fisheye lens and a convex mirror.带有鱼眼镜头和凸面镜的单相机全景立体成像系统。
Opt Express. 2011 Mar 28;19(7):5855-67. doi: 10.1364/OE.19.005855.