3D Reconstruction from Multiple Images
OpenCV provides the solvePnP() and solvePnPRansac() functions that implement this technique. 3.4 Multi View Stereo. The Multi View Stereo algorithms are used to
3D Reconstruction from Multiple Images
OpenCV provides the solvePnP() and solvePnPRansac() functions that implement this technique. 3.4 Multi View Stereo. The Multi View Stereo algorithms are used to
Line-Sweep: Cross-Ratio For Wide-Baseline Matching and 3D
[20] showed that connectivity constraints can be very useful for obtaining accurate line reconstruction from multiple images. Hofer et al. [19] showed
Comparing 3D Reconstruction from iPhone images from multiple
The specific implementations I plan to use and evaluate are the. OpenCV stereo reconstruction infrastructure the Structure- from-Motion and the necessary
Automated 3D Face Reconstruction From Multiple Images Using
Automated 3D reconstruction of faces from images is challenging if the image material is difficult in terms of pose lighting
EECS 442 Final Project: Structure for Motion
“Oscar Padierna - Stereo 3D. Reconstruction with OpenCV Using an IPhone Camera. [3] [3] “3D Reconstruction from Multiple Images.” Wikipedia. Wikimedia.
MASTERARBEIT MULTIVIEW 3D SHAPE RECONSTRUCTION
2 дек. 2021 г. with images from multiple viewpoints so that they can better reconstruct the 3D geometry of the object present in the images. The goal of ...
3D Scene Reconstruction Using Multiple 2D Images
We used a phone camera (Poco F2 pro) and used images of a checkerboard to calibrate the camera and obtain the camera matrix. We initially use pictures of a
3D reconstruction from multiple RGB-D images with different
3D model reconstruction can be a useful tool for multiple purposes. Some examples are modeling a person or objects for an animation in robotics
3D Reconstruction from Multiple Images
OpenCV provides the solvePnP() and solvePnPRansac() functions that implement this technique. 3.4 Multi View Stereo. The Multi View Stereo algorithms are used to
Methods for 3D Reconstruction from Multiple Images
3D scanners: costly and cumbersome [Lhuillier 02] ECCV'02 Quasi-Dense Reconstruction from Image Sequence. ... There are several different 3D models.
Relative 3D Reconstruction Using Multiple Uncalibrated Images
31 ??? 2011 ?. Faugeras (1992) published an insightful algebraic method to perform 3D projective reconstruction with the tricky use of the epipolar geometry of ...
Image matching for 3D reconstruction using complementary optical
29 ???. 2018 ?. Appariement d'images pour la reconstruction 3D par complémentarité optique et géométrique ... 1.1 Multi-view stereo for 3D reconstruction .
Thèse de Doctorat
Main goal: From multiple images obtained with uncalibrated Scanning Electron. Microscope develop a method allowing 3D reconstruction of objects with an
Efficient Dense 3D Reconstruction Using Image Pairs
The 3D reconstruction of a scene from 2D images is an important topic in the field of. Computer Vision due to the high demand in various applications such
MASTERARBEIT MULTIVIEW 3D SHAPE RECONSTRUCTION
Tasks such as inferring the 3D shape from multiple images have also gained immense popularity recently due to the breakthroughs in the field of 3D deep learning
3D Reconstruction Using a Linear Laser Scanner and A Camera
Then it uses the vision sensor for image acquisition so as to obtain the structured light image projection information of the surface of the object to be
AN ALGORITHM FOR RECONSTRUCTING THREE-DIMENSIONAL
there are often multiple cameras present that have overlapping fields of view. These digital images 3d reconstruction of stereo images for interaction.
3D DATA ACQUISITION BASED ON OPENCV FOR CLOSE-RANGE
6 ???. 2017 ?. the images resulted in the increased popularity of the photogrammetry. Algorithms for the 3D model reconstruction are so advanced.
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◦X 5 ...to my loving and amazing grandmother 6Acknowledgment
I would like to take this opportunity to thank the people who have supported, encour- aged, and inspired me in the process of writing my thesis. You made all the dierence. First, I must thank my supervisors, Dr. Sounkalo Dembele and Dr. Nadine Piat, for oering me this Ph.D. position. Your continuous support and trust helped me a lot during these three years. I will never thank you enough for the condence you had in me, your guidance, and advice throughout this Ph.D. I want to thank all the people in AS2M department of FEMTO-ST Institute who were always there for me. Patrick Rougeot, Jean-Yves Rauch, Guillaume Laurent, Olivier Lehmann, Cedric Clevy, and Brahim Tamadazte (order is arbitrary): thanks a lot! I cannot but mention all my colleagues who contributed in that great atmosphere where I have had an honor and a pleasure to work: Vincent Trenchant, Margot Billot, Houari Bettahar, Elodie Lechartier, Adrian Ciubotariu, Marcelo Gaudenzi de Faria, Mohamed Taha Chikhaoui, Mouloud Ourak, Bassem Dahroug, Benoit Brazey and I have certainly forgotten somebody... I express my sincere gratitude to Dr. Peter Sturm and Dr. Jacques Ganglo for accepting to be the referees of the present work and devoting time to carefully read this manuscript. I am sure that your suggestions, both in your written reports and during the defense, have helped me to improve this work. And I convey my heartfelt thanks to all other members of the jury: Dr. Olivier Haeberle and Dr. Cedric Demonceaux. Last, but by no means the least, I want to thank all my family: my grandmother Lina, my parents Vladislav and Irina, my sister Olga and her husband Roma, my niece and nephew, Lena and Denis. And of course, I thank my dearly loved Tanya, who kept me fed and smiling, and supported me in times of stress and frustration. You are the best! 8Contents
Mathematical symbols
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Abbreviations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Main notations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Introduction
15Thesis outline
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Background of 3D reconstruction in SEM
231.1 Image formation: physics
. . . . . . . . . . . . . . . . . . . . . . . . . . 241.2 Image formation: geometry
. . . . . . . . . . . . . . . . . . . . . . . . 291.2.1 Perspective camera
. . . . . . . . . . . . . . . . . . . . . . . . . 291.2.2 Ane camera
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 301.3 3D reconstruction in SEM
. . . . . . . . . . . . . . . . . . . . . . . . . 331.3.1 Calibration
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331.3.2 State of the art
. . . . . . . . . . . . . . . . . . . . . . . . . . . 341.4 Thesis goals
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 82 Motion estimation
412.1 Detection and matching of interest points
. . . . . . . . . . . . . . . . . 422.2 Camera modelling
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442.3 Estimating translation
. . . . . . . . . . . . . . . . . . . . . . . . . . . 462.4 Estimating rotation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472.4.1 Rotation matrix decomposition
. . . . . . . . . . . . . . . . . . 482.4.2 Two-view geometry
. . . . . . . . . . . . . . . . . . . . . . . . . 492.4.3 Bas-relief ambiguity
. . . . . . . . . . . . . . . . . . . . . . . . 512.4.4 Three-view geometry
. . . . . . . . . . . . . . . . . . . . . . . . 542.5 Experimental validation
. . . . . . . . . . . . . . . . . . . . . . . . . . 552.5.1 Synthetic images
. . . . . . . . . . . . . . . . . . . . . . . . . . 552.5.2 SEM images
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.6 Ane fundamental matrix
. . . . . . . . . . . . . . . . . . . . . . . . . 572.7 Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 33 Autocalibration
653.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.2 Intrinsic parameters
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683.3 Cost function formulation
. . . . . . . . . . . . . . . . . . . . . . . . . 693.3.1 Initial values
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 713.3.2 Bound constraints
. . . . . . . . . . . . . . . . . . . . . . . . . . 723.3.3 Regularization
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 7310 CONTENTS
3.4 Global optimization
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733.5 Experiments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 763.5.1 Robustness to noise
. . . . . . . . . . . . . . . . . . . . . . . . . 773.5.2 Convergence range
. . . . . . . . . . . . . . . . . . . . . . . . . 793.5.3 Real images
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793.6 Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 14 Dense 3D reconstruction
854.1 Introduction
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 864.2 Rectication
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874.2.1 Image transformation
. . . . . . . . . . . . . . . . . . . . . . . . 884.2.2 Experiments and analysis
. . . . . . . . . . . . . . . . . . . . . 884.3 Dense matching
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.4 Triangulation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954.5 Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 75 Towards automatic image acquisition
1015.1 Problem statement
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.2 Dynamic autofocus
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025.2.1 Sharpness optimization
. . . . . . . . . . . . . . . . . . . . . . . 1045.2.2 Experiments
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1065.3 Robot and tool center point calibration
. . . . . . . . . . . . . . . . . . 1105.3.1 Point-link calibration
. . . . . . . . . . . . . . . . . . . . . . . . 1125.3.2 Maintaining object location
. . . . . . . . . . . . . . . . . . . . 1135.3.3 Results
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1145.4 Tool center point calibration
. . . . . . . . . . . . . . . . . . . . . . . . 1155.5 Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1176 Software development
1196.1 Context
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1206.2 Pollen3D software GUI
. . . . . . . . . . . . . . . . . . . . . . . . . . . 1216.2.1 Image tab
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1226.2.2 Stereo tab
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1236.2.3 Multiview tab
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1246.3 Conclusion
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Conclusion and perspectives
1276.4 Summary and discussion
. . . . . . . . . . . . . . . . . . . . . . . . . . 12 76.5 Contributions
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1286.6 Future work
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 9Bibliography
130Appendices
141Appendix A.
Exp erimentalsetup
141Appendix B.
Camera vs ob jectmot ion
143Appendix C.
Diamond: syn theticima gedata
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