Mobile Augmented Reality for Semantic 3D Models -A Smartphone-Based Approach with Citygml
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Camera Motion Estimation for Multi-Camera Systems
Camera Motion Estimation for Multi-Camera Systems Jae-Hak Kim A thesis submitted for the degree of Doctor of Philosophy of The Australian National University August 2008 This thesis is submitted to the Department of Information Engineering, Research School of Information Sciences and Engineering, The Australian National University, in fullfilment of the requirements for the degree of Doctor of Philosophy. This thesis is entirely my own work, except where otherwise stated, describes my own research. It contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning. Jae-Hak Kim 31 July 2008 Supervisory Panel: Prof. Richard Hartley The Australian National University Dr. Hongdong Li The Australian National University Prof. Marc Pollefeys ETH, Z¨urich Dr. Shyjan Mahamud National ICT Australia In summary, this thesis is based on materials from the following papers, and my per- cived contributions to the relevant chpaters of my thesis are stated: Jae-Hak Kim and Richard Hartley, “Translation Estimation from Omnidirectional Images,” Digital Im- age Computing: Technqiues and Applications, 2005. DICTA 2005. Proceedings, vol., no., pp. 148-153, Dec 2005, (80 per cent of my contribution and related to chapter 6) Brian Clipp, Jae-Hak Kim, Jan-Michael Frahm, Marc Pollefeys and Richard Hartley, “Robust 6DOF Motion Estimation for Non-Overlapping, Multi-Camera Systems,” Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on , vol., no., pp.1-8, 7-9 Jan 2008, (40 per cent of my contribu- tion and related to chapter 7) Jae-Hak Kim, Richard Hartley, Jan-Michael and Marc Pollefeys, “Visual Odometry for Non-overlapping Views Using Second-Order Cone Programming,” Asian Conference on Computer Vision, Tokyo, Japan, ACCV (2) 2007, pp. -
2020Spring 20 Camerasandca
Cats + mirrors + face filters [reddit – juicysox] [Isa Milefchik (1430 HTA Spring 2020)] [Madelyn Adams (student Spring 2019)] Zoom protocol Please: • Cameras on (it really helps me to see you) • Real names • Mics muted • Raise hands in Zoom for questions, unmute when I call • I will ask more often for questions Project 4 – due Friday • Both parts – Written – Code Project 5 • Questions and code due Friday April 10th Final group project • Groups of four • Groups of one are discouraged – you need a good reason. • Group by timezone where possible; use Piazza • We’ll go over possible projects at a later date Questions • What else did I miss? By Suren Manvelyan, http://www.surenmanvelyan.com/gallery/7116 By Suren Manvelyan, http://www.surenmanvelyan.com/gallery/7116 By Suren Manvelyan, http://www.surenmanvelyan.com/gallery/7116 What is a camera? Camera obscura: dark room Known during classical period in China and Greece (e.g., Mo-Ti, China, 470BC to 390BC) Illustration of Camera Obscura Freestanding camera obscura at UNC Chapel Hill Photo by Seth Ilys James Hays James, San Francisco, Aug. 2017 Camera obscura / lucida used for tracing Lens Based Camera Obscura, 1568 Camera lucida drawingchamber.wordpress.com Tim’s Vermeer Vermeer, The Music Lesson, 1665 Tim Jenison (Lightwave 3D, Video Toaster) Tim’s Vermeer – video still First Photograph Oldest surviving photograph Photograph of the first photograph – Took 8 hours on pewter plate Joseph Niepce, 1826 Stored at UT Austin Niepce later teamed up with Daguerre, who eventually created Daguerrotypes Dimensionality Reduction Machine (3D to 2D) 3D world 2D image Point of observation Figures © Stephen E. -
Pinhole Camera Calibration in the Presence of Human Noise
Linköping Studies in Science and Technology Dissertations, No. 1402 Pinhole Camera Calibration in the Presence of Human Noise Magnus Axholt Department of Science and Technology Linköping University SE-601 74 Norrköping, Sweden Norrköping, 2011 Pinhole Camera Calibration in the Presence of Human Noise Copyright © 2011 Magnus Axholt [email protected] Division of Visual Information Technology and Applications (VITA) Department of Science and Technology, Linköping University SE-601 74 Norrköping, Sweden ISBN 978-91-7393-053-6 ISSN 0345-7524 This thesis is available online through Linköping University Electronic Press: www.ep.liu.se Printed by LiU-Tryck, Linköping, Sweden 2011 Abstract The research work presented in this thesis is concerned with the analysis of the human body as a calibration platform for estimation of a pinhole camera model used in Aug- mented Reality environments mediated through Optical See-Through Head-Mounted Display. Since the quality of the calibration ultimately depends on a subject’s ability to construct visual alignments, the research effort is initially centered around user studies investigating human-induced noise, such as postural sway and head aiming precision. Knowledge about subject behavior is then applied to a sensitivity analy- sis in which simulations are used to determine the impact of user noise on camera parameter estimation. Quantitative evaluation of the calibration procedure is challenging since the current state of the technology does not permit access to the user’s view and measurements in the image plane as seen by the user. In an attempt to circumvent this problem, researchers have previously placed a camera in the eye socket of a mannequin, and performed both calibration and evaluation using the auxiliary signal from the camera. -
Calibration Using a General Homogeneous Depth Camera Model
DEGREE PROJECT IN THE FIELD OF TECHNOLOGY ENGINEERING PHYSICS AND THE MAIN FIELD OF STUDY ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2017 Calibration using a general homogeneous depth camera model DANIEL SJÖHOLM KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF COMPUTER SCIENCE AND COMMUNICATION Calibration using a general homogeneous depth camera model DANIEL SJÖHOLM Master’s Thesis at CSC/CVAP Supervisor: Magnus Burenius Supervisor at KTH: Patric Jensfelt Examiner: Joakim Gustafson Abstract Being able to accurately measure distances in depth images is important for accurately reconstructing objects. But the measurement of depth is a noisy process and depth sensors could use additional correction even after factory calibration. We regard the pair of depth sensor and image sensor to be one single unit, returning complete 3D information. The 3D information is combined by relying on the more accurate image sensor for everything except the depth measurement. We present a new linear method of correcting depth distortion, using an empirical model based around the con- straint of only modifying depth data, while keeping planes planar. The depth distortion model is implemented and tested on the Intel RealSense SR300 camera. The results show that the model is viable and generally decreases depth measurement errors after calibrating, with an average improvement in the 50 % range on the tested data sets. Referat Kalibrering av en generell homogen djupkameramodell Att noggrant kunna mäta avstånd i djupbilder är viktigt för att kunna göra bra rekonstruktioner av objekt. Men denna mätprocess är brusig och dagens djupsensorer tjänar på ytterligare korrektion efter fabrikskalibrering. Vi betraktar paret av en djupsensor och en bildsensor som en enda enhet som returnerar komplett 3D information.