Registration between multiple sequences for scene reconstruction

YANG Lei; LI Gui-ju; Wang Li-rong

doi:10.3788/OPE.20152302.0557

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Registration between multiple sequences for scene reconstruction

更新时间：2020-08-13

- Registration between multiple sequences for scene reconstruction
- Optics and Precision Engineering Vol. 23, Issue 2, Pages: 557-565(2015)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
  3. 长春大学电子信息与工程学院,吉林长春,130022
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152302.0557
  CLC： TP391.4
- Received：15 November 2013，
  
  Revised：23 December 2013，
  
  Published：25 February 2015
- 稿件说明：
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杨磊, 李桂菊, 王丽荣. 面向场景重构的多序列间配准[J]. 光学精密工程, 2015,23(2): 557-565

YANG Lei, LI Gui-ju, Wang Li-rong. Registration between multiple sequences for scene reconstruction[J]. Editorial Office of Optics and Precision Engineering, 2015,23(2): 557-565
杨磊, 李桂菊, 王丽荣. 面向场景重构的多序列间配准[J]. 光学精密工程, 2015,23(2): 557-565 DOI： 10.3788/OPE.20152302.0557.

YANG Lei, LI Gui-ju, Wang Li-rong. Registration between multiple sequences for scene reconstruction[J]. Editorial Office of Optics and Precision Engineering, 2015,23(2): 557-565 DOI： 10.3788/OPE.20152302.0557.

摘要

针对传统的单序列扩展式场景重构方法易丢失场景信息

数据利用率低下等问题

提出一种面向场景重构的多序列间配准的方法。该方法采用相似变换作为配准模型

完成对不同参考坐标系下尺度、旋转、平移相分离的初始配准;然后通过拟合观测平面来抑制噪声点并筛选公共可见点;最终对不同尺度、方位的序列进行配准

配准结果可直接用于后续的重构中。实验表明:通过相应噪声抑制

使得各序列的系统初始重投影误差降低了17.69%到46.86%

终止重投影误差降低了27.5%到71.96%。配准后从相同的47幅图像中可重构10 596个场景点

相比传统单序列方法的3 893个场景点

该方法更充分有效地利用了观测图像

使最终拟合的场景曲面包含了更多的场景细节。

Abstract

As traditional single sequence scene reconstruction method is easy to loss scene information and has a lower data utilization

this paper proposes an approach to the registration between multiple sequences for scene reconstruction. The method uses a similarity transformational as the registration model and proposes the separating initial registration method to deal with the scale

rotation and translation respectively. Then

it fits the observed plane to suppress the noise data and select the co-visible points. Finally

the registration between multiple sequences with different scales

positions is performed and the registration results are applied in successive subsequent reconstruction. The experiment shows that the initial error has been reduced from 17.69% to 46.86%

and the terminated error reduced from 27.5% to 71.96% after suppression of noise data along the oriented orientation. Moreover

the 10 596 vertices are reconstructed from 47 frame images

more better than 3 893 vertices reconstructed by traditional single sequential method. The proposed method full makes use of the observation images and allows the ultimate fitting surface of the scene to have more scene details.

关键词

Keywords

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