基于2D-3D泛轮廓点对应的三维刚体目标的迭代姿态估计

doi:10.7523/j.issn.2095-6134.2012.6.015

中国科学院大学学报 ›› 2012, Vol. ›› Issue (6): 821-828.DOI: 10.7523/j.issn.2095-6134.2012.6.015

基于2D-3D泛轮廓点对应的三维刚体目标的迭代姿态估计

冷大炜^1,2, 马洪兵¹, 孙卫东¹

1. 清华大学电子工程系, 北京 100084;
2. 中国科学院研究生院信息科学与工程学院, 北京 100049

收稿日期:2011-05-13 修回日期:2011-11-08 发布日期:2012-11-15
通讯作者: 冷大炜
基金资助:
国家自然科学基金(60872083)资助

Iterative pose estimation of a 3D rigid object based on general 2D-3D contour point correspondence

LENG Da-Wei^1,2, MA Hong-Bing¹, SUN Wei-Dong¹

1. Electronic Engineering Department, Tsinghua University, Beijing 100084, China;
2. School of Information Science and Engineering, Graduate University, Chinese Academy of Sciences, Beijing 100049, China

Received:2011-05-13 Revised:2011-11-08 Published:2012-11-15

摘要/Abstract

摘要： 以单目观测下三维刚体目标的姿态估计为研究对象,针对现有迭代估计方法存在的收敛半径小和收敛速度慢的问题,提出一种新的基于2D-3D泛轮廓点对应的迭代姿态估计方法.与现有的基于数值优化的方法不同,本方法从输入图像的2D泛轮廓点出发,着眼于显性地建立输入图像到目标三维模型的2D-3D特征投影对应关系,进而以此显性投影对应关系对目标的三维姿态参数进行估计.实验结果表明,该方法在算法复杂性、收敛半径和收敛速度上均有明显改进.

关键词: 三维刚体目标, 单目视觉, 姿态估计, 特征投影对应关系

Abstract: Pose estimation of 3D rigid object based on monocular vision is studied. Considering the insufficiency of state-of-art iterative methods in convergence radius and convergence speed, we propose a new iterative pose estimation method based on general 2D-3D contour point correspondence. The proposed method focuses on explicitly establishing the 2D-3D feature projection correspondence between the input image and object's 3D model with the general 2D contour points of the input image, and then estimates object's 3D pose parameters. Experiment results show that the proposed method provides notable improvements in algorithm complexity, convergence radius, and convergence speed.

Key words: 3D rigid object, monocular vision, pose estimation, feature projection correspondence

中图分类号:

TP391

冷大炜, 马洪兵, 孙卫东. 基于2D-3D泛轮廓点对应的三维刚体目标的迭代姿态估计[J]. 中国科学院大学学报, 2012, (6): 821-828.

LENG Da-Wei, MA Hong-Bing, SUN Wei-Dong. Iterative pose estimation of a 3D rigid object based on general 2D-3D contour point correspondence[J]. , 2012, (6): 821-828.

参考文献

[1] Lu C P, Hager G D, Mjolsness E. Fast and globally convergent pose estimation from video images [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(6): 610-622.
[2] Iwashita Y, Kurazume R, Konishi K, et al. Fast alignment of 3D geometrical models and 2D grayscale images using 2D distance maps [J]. Systems and Computers in Japan, 2007, 38(4): 1889-1899.
[3] Dambreville S, Sandhu R, Yezzi A, et al. A geometric approach to joint 2D region-based segmentation and 3D pose estimation using a 3D shape prior [J]. SIAM Journal on Imaging Sciences, 2010, 3(1): 110-132.
[4] Haralick R M, Lee C, Ottenberg K, et al. Review and analysis of solution of the three point perspective pose estimation problem [J]. International Journal of Computer Vision, 1994, 13(3): 331-356.
[5] Noguer F M, Lepetit V, Fua P. Accurate non-iterative O(n) solution to the PnP problem [C]//Proceedings of International Conference on Computer Vision, IEEE. 2007: 1-8.
[6] Leng D W, Sun W D. Finding all the solutions of PnP problem [C]//Proceedings of International Workshop on Imaging Systems and Techniques, IEEE. 2009: 348-352.
[7] Dunker J, Hartmann G, Stöhr M. Single view recognition and pose estimation of 3D objects using sets of prototypical views and spatially tolerant contour representations [C]//Proceedings of International Conference on Pattern Recognition. 1996: 14-18.
[8] Hartley R, Zisserman A. Multiple view geometry in computer vision [M]. 2nd ed. United Kingdom: Cambridge University Press, 2003.
[9] Felzenszwalb P F, Huttenlocher D P. Distance transforms of sampled functions [R]. Cornell Computing and Information Science TR2004-1963, 2004.
[10] Fabbri R, Costa L D F, Torelli J C, et al. 2D Euclidean distance transform algorithms: a comparative survey [J]. ACM Computing Surveys, 2008, 40(1): 1-44.
[11] Osher S, Fedkiw R. Level set methods and dynamic implicit surfaces [M]. Springer, 2003.
[12] Struik D J. Lectures on classical differential geometry [M]. 2nd ed. New York: Dover Publications, Inc, 1961.
[13] Smith S M, Brady J M. SUSAN—a new approach to low level image processing [J]. International Journal of Computer Vision, 1997, 23(1): 45-78.
[14] Bertsekas D P. Constrained optimization and Lagrange multiplier methods [M]. Boston: Academic Press, 1982.

[1]	杨雨龙, 郭田德, 韩丛英. 基于原型学习改进的伪标签半监督学习算法[J]. 中国科学院大学学报, 2021, 38(6): 841-851.
[2]	史达亨, 刘立刚, 周斌, 卜智勇. 跨时间迁移的多源无线信号指纹融合定位方法[J]. 中国科学院大学学报, 2021, 38(6): 817-824.
[3]	庄子俊, 袁晓兵, 裴俊, 王国辉, 刘建坡. 基于无监督表达学习的森林地貌特征建模及林火易发性评估[J]. 中国科学院大学学报, 0, (): 1-1.
[4]	陈汉钦, 秦进, 赵彤, 阎瑶. 面向识别的人脸三维信息估计^*[J]. 中国科学院大学学报, 0, (): 2-2.
[5]	赵敏钧, 赵亚伟, 赵雅捷, 罗刚. 一种新的基于深度学习的重叠关系联合抽取模型[J]. 中国科学院大学学报, 0, (): 55-55.
[6]	马曌月, 肖俊, 王颖. 基于面提取的三维岩体点云孔洞检测与修复方法^*[J]. 中国科学院大学学报, 0, (): 64-64.
[7]	林大权, 范睿, 张良峰. 基于高维特征的图像对抗攻击算法^*[J]. 中国科学院大学学报, 0, (): 74-74.
[8]	王贺, 卜智勇, 谭冲. 基于核相关滤波视觉检测的多旋翼无人机对地目标跟踪与逼近[J]. 中国科学院大学学报, 0, (): 21532-21532.
[9]	赵心驰, 姜策, 何为. 基于注意力生成对抗网络的图像强光去除[J]. 中国科学院大学学报, 0, (): 21548-21548.
[10]	马莹莹, 吴幼龙, 唐华. 基于特征编码和图嵌入的姓名消歧方法[J]. 中国科学院大学学报, 0, (): 21549-21549.
[11]	张韬, 肖俊, 王颖. 基于匹配点逐层过滤的岩体点云配准方法[J]. 中国科学院大学学报, 0, (): 21550-21550.
[12]	杨善强, 李华旺, 常亮, 高才栋, 虞业泺. 一种基于RPC的组态化卫星模拟器故障注入方法[J]. 中国科学院大学学报, 2021, 38(3): 402-408.
[13]	盛晓光, 王颖, 张迎伟, 项若曦, 付红萍. WILS：面向学业预警的非均衡增量式学习方法*[J]. 中国科学院大学学报, 0, (): 17-18.
[14]	周龙, 王伟强, 吕科. FACR：一种快速且准确的车辆识别器[J]. 中国科学院大学学报, 2021, 38(1): 130-136.
[15]	胡莉娜, 高盛华. 基于层次化标签的人体解析[J]. 中国科学院大学学报, 2020, 37(6): 820-827.

基于2D-3D泛轮廓点对应的三维刚体目标的迭代姿态估计

Iterative pose estimation of a 3D rigid object based on general 2D-3D contour point correspondence

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

访问统计

联系我们