Minimal Vision System for Linear Camera Self-Calibration and 3D Reconstruction Using Translation Platform
Minimal Vision System for Linear Camera Self-Calibration and 3D Reconstruction Using Translation Platform
复制标题
使用平移平台进行线性相机自校准和 3D 重建的最小视觉系统
DOI:
10.1007/s11042-019-7666-5
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发表时间:
2019-05
影响因子:
3.6
通讯作者:
赵斌
中科院分区:
文献类型:
--
作者:
胡钊政;赵斌
Camera calibration and 3D reconstruction are two crucial steps in computer vision. With the.progress of robot and autonomous rover systems, zooming cameras are widely applied, where.online calibration and 3D reconstruction are becoming more and more important. This paper.proposed a minimal vision system that consists of a translation platform and an un-calibrated.camera mounted on.With this minimal system, we can linearly calibrate the camera online and.reconstruct 3D structures from three un-calibrated images by utilizing translation motions. The.two images generated by translating the camera to allow the recovery of scene depths. Depths.are then analyzed by error analysis models and are utilized to determine the infinite.homography between the third image and any of the two translated images. The intrinsic.parameters are then calibrated linearly from the computed infinite homography. Camera.motion estimation and 3D reconstruction are then readily determined from the intrinsic.calibration results. We also proposed a two-step optimization method to refine both the.calibration and 3D reconstruction results by minimizing the overall back-projection errors.across the three images within a tiny-scale bundle adjustment framework. The proposed.method has been validated with both simulation and real image data. The results demonstrate.that the proposed minimal linear system can solve the online camera self-calibration problem.and the reconstruction of 3D structure. The paper suggests a framework of minimal linear.system by using an un-calibrated camera and a low-cost translation platform to address the.problems of linear self-calibration of camera, motion estimation, 3D reconstruction, and.optimization, in a practical, easy, and accurate way.
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DOI:
10.1109/imtc.2011.5944093
发表时间:
2011-05
期刊:
2011 IEEE International Instrumentation and Measurement Technology Conference
影响因子:
--
作者:
Qiang Wang;Yan Liu;Yi Shen
通讯作者:
Qiang Wang;Yan Liu;Yi Shen
影响因子:
3.6
作者:
K. Kottari;K. Delibasis;V. Plagianakos
通讯作者:
K. Kottari;K. Delibasis;V. Plagianakos
DOI:
10.1109/70.481755
发表时间:
1996-02-01
期刊:
IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION
影响因子:
--
作者:
DeMa, S
通讯作者:
DeMa, S
DOI:
10.1145/1486525.1486526
发表时间:
2009-03
期刊:
ACM Trans. Math. Softw.
影响因子:
--
作者:
Jan Mayer
通讯作者:
Jan Mayer
影响因子:
2.7
作者:
Lourakis, Manolis I. A.;Argyros, Antonis A.
通讯作者:
Argyros, Antonis A.