Robust refinement methods for camera calibration and 3D reconstruction from multiple images-Reference-Cited by-同舟云学术

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Robust refinement methods for camera calibration and 3D reconstruction from multiple images

Published:2011-06 Issue:8 Volume:32 Page:1210-1221
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Hor Maw-Kae,Tang Cheng-Yuan,Wu Yi-Leh,Chan Kai-Hsuan,Tsai Jeng-Jiun

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference22 articles.

1. Aliaga, D.G., Zhang, J., Boutin, M., 2007. Simplifying the reconstruction of 3D models using parameter elimination. In: VRML Workshop, 11th Internat. Conf. on Computer Vision, pp. 1–8.

2. Advances in computational stereo;Brown;IEEE Trans. Pattern Anal. Machine Intell.,2003

3. Cornelius, H., Sára, R., Martinec, D., Pajdla, T., Chum, O., Matas, J., 2004. Towards complete free-form reconstruction of complex 3D scenes from an unordered set of uncalibrated images. In: ECCV Workshop SMVP, pp. 1–12.

4. Faugeras, O., 1992. What can be seen in three dimensions with an uncalibrated stereo rig? In: Proc. ECCV, LNCS, vol. 588. pp. 563–578.

5. Furukawa, Y., Ponce, J., 2007a. Accurate, dense, and robust multi-view stereopsis. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–8.

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1. Three dimensional measurement using likelihood function by multi-camera;SPIE Proceedings;2017-03-13

2. A Method for the 3D Reconstruction Based on Edge Detection and Feature Extraction;Transactions on Edutainment XI;2015

3. Using concentric circles and wedge grating for camera calibration;Applied Optics;2012-06-07

4. System Identification: 3D Measurement Using Structured Light System;Advanced Concepts for Intelligent Vision Systems;2012

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