Photography, Mobile, and Immersive Imaging 2018
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Bibliography Thomas Annen, Jan Kautz, Frédo Durand, and Hans-Peter Seidel. Spherical harmonic gradients for mid-range illumination. In Alexander Keller and Henrik Wann Jensen, editors, Eurographics Symposium on Rendering, pages 331–336, Norrköping, Sweden, 2004. Eurographics Association. ISBN 3-905673-12-6. 35,72 Arthur Appel. Some techniques for shading machine renderings of solids. In AFIPS 1968 Spring Joint Computer Conference, volume 32, pages 37–45, 1968. 20 Okan Arikan, David A. Forsyth, and James F. O’Brien. Fast and detailed approximate global illumination by irradiance decomposition. In ACM Transactions on Graphics (Proceedings of SIGGRAPH 2005), pages 1108–1114. ACM Press, July 2005. doi: 10.1145/1073204.1073319. URL http://doi.acm.org/10.1145/1073204.1073319. 47, 48, 52 James Arvo. The irradiance jacobian for partially occluded polyhedral sources. In Computer Graphics (Proceedings of SIGGRAPH 94), pages 343–350. ACM Press, 1994. ISBN 0-89791-667-0. doi: 10.1145/192161.192250. URL http://dx.doi.org/10.1145/192161.192250. 72, 102 Neeta Bhate. Application of rapid hierarchical radiosity to participating media. In Proceedings of ATARV-93: Advanced Techniques in Animation, Rendering, and Visualization, pages 43–53, Ankara, Turkey, July 1993. Bilkent University. 69 Neeta Bhate and A. Tokuta. Photorealistic volume rendering of media with directional scattering. In Alan Chalmers, Derek Paddon, and François Sillion, editors, Rendering Techniques ’92, Eurographics, pages 227–246. Consolidation Express Bristol, 1992. 69 Miguel A. Blanco, M. Florez, and M. Bermejo. Evaluation of the rotation matrices in the basis of real spherical harmonics. Journal Molecular Structure (Theochem), 419:19–27, December 1997. -
Efficient Ray Tracing of Volume Data
Efficient Ray Tracing of Volume Data TR88-029 June 1988 Marc Levay The University of North Carolina at Chapel Hill Department of Computer Science CB#3175, Sitterson Hall Chapel Hill, NC 27599-3175 UNC is an Equal Opportunity/ Affirmative Action Institution. 1 Efficient Ray Tracing of Volume Data Marc Levoy June, 1988 Computer Science Department University of North Carolina Chapel Hill, NC 27599 Abstract Volume rendering is a technique for visualizing sampled scalar functions of three spatial dimen sions without fitting geometric primitives to the data. Images are generated by computing 2-D pro jections of a colored semi-transparent volume, where the color and opacity at each point is derived from the data using local operators. Since all voxels participate in the generation of each image, rendering time grows linearly with the size of the dataset. This paper presents a front-to-back image-order volume rendering algorithm and discusses two methods for improving its performance. The first method employs a pyramid of binary volumes to encode coherence present in the data, and the second method uses an opacity threshold to adaptively terminate ray tracing. These methods exhibit a lower complexity than brute-force algorithms, although the actual time saved depends on the data. Examples from two applications are given: medical imaging and molecular graphics. 1. Introduction The increasing availability of powerful graphics workstations in the scientific and computing communities has catalyzed the development of new methods for visualizing discrete multidimen sional data. In this paper, we address the problem of visualizing sampled scalar functions of three spatial dimensions, henceforth referred to as volume data. -
Lecture 2 3D Modeling
Lecture 2 3D Modeling Dr. Shuang LIANG School of Software Engineering Tongji University Fall 2012 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 Lecturer Dr. Shuang LIANG • Assisstant professor, SSE, Tongji – Education » B.Sc in Computer Science, Zhejiang University, 1999-2003 » PhD in Computer Science, Nanjing Univerisity, 2003-2008 » Visit in Utrecht University, 2007, 2008 – Research Fellowship » The Chinese University of Hong Kong, 2009 » The Hong Kong Polytechnic University, 2010-2011 » The City University of Hong Kong, 2012 • Contact – Office: Room 442, JiShi Building, JiaDing Campus, TongJi (Temporary) – Email: [email protected] 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 Outline • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 Outline • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 What is a 3D model? 3D object using a collection of points in 3D space, connected by various geometric entities such as triangles, lines, curved surfaces, etc. It is a collection of data (points and other information) 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Fall 2012 What is a 3D modeling? The process of developing a mathematical representation of any three-dimensional -
Volume Rendering on Scalable Shared-Memory MIMD Architectures
Volume Rendering on Scalable Shared-Memory MIMD Architectures Jason Nieh and Marc Levoy Computer Systems Laboratory Stanford University Abstract time. Examples of these optimizations are hierarchical opacity enumeration, early ray termination, and spatially adaptive image Volume rendering is a useful visualization technique for under- sampling [13, 14, 16, 181. Optimized ray tracing is the fastest standing the large amounts of data generated in a variety of scien- known sequential algorithm for volume rendering. Nevertheless, tific disciplines. Routine use of this technique is currently limited the computational requirements of the fastest sequential algorithm by its computational expense. We have designed a parallel volume still preclude real-time volume rendering on the fastest computers rendering algorithm for MIMD architectures based on ray tracing today. and a novel task queue image partitioning technique. The combi- Parallel machines offer the computational power to achieve real- nation of ray tracing and MIMD architectures allows us to employ time volume rendering on usefully large datasets. This paper con- algorithmic optimizations such as hierarchical opacity enumera- siders how parallel computing can be brought to bear in reducing tion, early ray termination, and adaptive image sampling. The use the computation time of volume rendering. We have designed of task queue image partitioning makes these optimizations effi- and implemented an optimized volume ray tracing algorithm that cient in aparallel framework. We have implemented our algorithm employs a novel task queue image partitioning technique on the on the Stanford DASH Multiprocessor, a scalable shared-memory Stanford DASH Multiprocessor [lo], a scalable shared-memory MIMD machine. Its single address-space and coherent caches pro- MIMD (Multiple Instruction, Multiple Data) machine consisting vide programming ease and good performance for our algorithm. -
Computational Photography
Computational Photography George Legrady © 2020 Experimental Visualization Lab Media Arts & Technology University of California, Santa Barbara Definition of Computational Photography • An R & D development in the mid 2000s • Computational photography refers to digital image-capture cameras where computers are integrated into the image-capture process within the camera • Examples of computational photography results include in-camera computation of digital panoramas,[6] high-dynamic-range images, light- field cameras, and other unconventional optics • Correlating one’s image with others through the internet (Photosynth) • Sensing in other electromagnetic spectrum • 3 Leading labs: Marc Levoy, Stanford (LightField, FrankenCamera, etc.) Ramesh Raskar, Camera Culture, MIT Media Lab Shree Nayar, CV Lab, Columbia University https://en.wikipedia.org/wiki/Computational_photography Definition of Computational Photography Sensor System Opticcs Software Processing Image From Shree K. Nayar, Columbia University From Shree K. Nayar, Columbia University Shree K. Nayar, Director (lab description video) • Computer Vision Laboratory • Lab focuses on vision sensors; physics based models for vision; algorithms for scene interpretation • Digital imaging, machine vision, robotics, human- machine interfaces, computer graphics and displays [URL] From Shree K. Nayar, Columbia University • Refraction (lens / dioptrics) and reflection (mirror / catoptrics) are combined in an optical system, (Used in search lights, headlamps, optical telescopes, microscopes, and telephoto lenses.) • Catadioptric Camera for 360 degree Imaging • Reflector | Recorded Image | Unwrapped [dance video] • 360 degree cameras [Various projects] Marc Levoy, Computer Science Lab, Stanford George Legrady Director, Experimental Visualization Lab Media Arts & Technology University of California, Santa Barbara http://vislab.ucsb.edu http://georgelegrady.com Marc Levoy, Computer Science Lab, Stanford • Light fields were introduced into computer graphics in 1996 by Marc Levoy and Pat Hanrahan. -
Computational Photography Research at Stanford
Computational photography research at Stanford January 2012 Marc Levoy Computer Science Department Stanford University Outline ! Light field photography ! FCam and the Stanford Frankencamera ! User interfaces for computational photography ! Ongoing projects • FrankenSLR • burst-mode photography • languages and architectures for programmable cameras • computational videography and cinematography • multi-bucket pixels 2 ! Marc Levoy Light field photography using a handheld plenoptic camera Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz and Pat Hanrahan (Proc. SIGGRAPH 2005 and TR 2005-02) Light field photography [Ng SIGGRAPH 2005] ! Marc Levoy Prototype camera Contax medium format camera Kodak 16-megapixel sensor Adaptive Optics microlens array 125µ square-sided microlenses 4000 ! 4000 pixels ÷ 292 ! 292 lenses = 14 ! 14 pixels per lens Digital refocusing ! ! ! 2010 Marc Levoy Example of digital refocusing ! 2010 Marc Levoy Refocusing portraits ! 2010 Marc Levoy Light field photography (Flash demo) ! 2010 Marc Levoy Commercialization • trades off (excess) spatial resolution for ability to refocus and adjust the perspective • sensor pixels should be made even smaller, subject to the diffraction limit, for example: 36mm ! 24mm ÷ 2.5µ pixels = 266 Mpix 20K ! 13K pixels 4000 ! 2666 pixels ! 20 ! 20 rays per pixel or 2000 ! 1500 pixels ! 3 ! 3 rays per pixel = 27 Mpix ! Marc Levoy Camera 2.0 Marc Levoy Computer Science Department Stanford University The Stanford Frankencameras Frankencamera F2 Nokia N900 “F” ! facilitate research in experimental computational photography ! for students in computational photography courses worldwide ! proving ground for plugins and apps for future cameras 14 ! 2010 Marc Levoy CS 448A - Computational Photography 15 ! 2010 Marc Levoy CS 448 projects M. Culbreth, A. Davis!! ! ! A phone-to-phone 4D light field fax machine J. -
Classic Models in Computer Graphics • 3D Model Representations • Raw Data • Solids • Surfaces
Lecture 2 3D Modeling Dr. Shuang LIANG School of Software Engineering Tongji University Spring 2013 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Today’s Topics • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Today’s Topics • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 What is a 3D model? 3D object using a collection of points in 3D space, connected by various geometric entities such as triangles, lines, curved surfaces, etc. It is a collection of data (points and other information) 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 What is a 3D modeling? The process of developing a mathematical representation of any three-dimensional surface of object via specialized software. 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Today’s Topics • What is a 3D model? • Usage of 3D models • Classic models in computer graphics • 3D model representations • Raw data • Solids • Surfaces 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Usage of a 3D model The medical industry uses detailed models of organs 3D Modeling, Advanced Computer Graphics Shuang LIANG, SSE, Spring 2013 Usage of a 3D model The movie industry uses them as characters -
Computational Photography & Cinematography
Computational photography & cinematography (for a lecture specifically devoted to the Stanford Frankencamera, see http://graphics.stanford.edu/talks/camera20-public-may10-150dpi.pdf) Marc Levoy Computer Science Department Stanford University The future of digital photography ! the megapixel wars are over (and it’s about time) ! computational photography is the next battleground in the camera industry (it’s already starting) 2 ! Marc Levoy Premise: available computing power in cameras is rising faster than megapixels 12 70 9 52.5 6 35 megapixels 3 17.5 billions of pixels / second of pixels billions 0 0 2005 2006 2007 2008 2009 2010 Avg megapixels in new cameras, CAGR = 1.2 NVIDIA GTX texture fill rate, CAGR = 1.8 (CAGR for Moore’s law = 1.5) ! this “headroom” permits more computation per pixel, 3 or more frames per second, or less custom hardware ! Marc Levoy The future of digital photography ! the megapixel wars are over (long overdue) ! computational photography is the next battleground in the camera industry (it’s already starting) ! how will these features appear to consumers? • standard and invisible • standard and visible (and disable-able) • aftermarket plugins and apps for your camera 4 ! Marc Levoy The future of digital photography ! the megapixel wars are over (long overdue) ! computational photography is the next battleground in the camera industry (it’s already starting) ! how will these features appear to consumers? • standard and invisible • standard and visible (and disable-able) • aftermarket plugins and apps for your camera -
Uva-DARE (Digital Academic Repository)
UvA-DARE (Digital Academic Repository) Interactive Exploration in Virtual Environments Belleman, R.G. Publication date 2003 Link to publication Citation for published version (APA): Belleman, R. G. (2003). Interactive Exploration in Virtual Environments. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:30 Sep 2021 References s [1]] 3rdTech webpage, http://www.3rdtech.com/. [2]] M.J. Ackerman. Accessing the visible human project. D-Lib Magazine: The MagazineMagazine of the Digital Library Forum, October 1995. [3]] H. Afsarmanesh, R.G. Belleman, A.S.Z. Belloum, A. Benabdelkader, J.F.J, vann den Brand, G.B. Eijkel, A. Frenkel, C. Garita, D.L. Groep, R.M.A. Heeren, Z.W.. Hendrikse, L.0. Hertzberger, J.A. Kaandorp, E.C. -
Multi-View Image Fusion
Multi-view Image Fusion Marc Comino Trinidad1 Ricardo Martin Brualla2 Florian Kainz2 Janne Kontkanen2 1Polytechnic University of Catalonia, 2Google Inputs Outputs Color Transfer Mono Color High-def mono Color High-def color HDR Fusion Color EV+2 Color EV-1 Color EV+2 Color EV-1 Denoised Detail Transfer DSLR Stereo DSLR Low-def stereo High-def stereo Figure 1: We present a method for multi-view image fusion that is a applicable to a variety of scenarios: a higher resolution monochrome image is colorized with a second color image (top row), two color images with different exposures are fused into an HDR lower-noise image (middle row), and a high quality DSLR image is warped to the lower quality stereo views captured by a VR-camera (bottom row). Abstract 1. Introduction In this paper we focus on the problem of fusing multiple We present an end-to-end learned system for fusing mul- misaligned photographs into a chosen target view. Multi- tiple misaligned photographs of the same scene into a cho- view image fusion has become increasingly relevant with sen target view. We demonstrate three use cases: 1) color the recent influx of multi-camera mobile devices. transfer for inferring color for a monochrome view, 2) HDR The form factor of these devices constrains the size of fusion for merging misaligned bracketed exposures, and 3) lenses and sensors, and this limits their light capturing abil- detail transfer for reprojecting a high definition image to ity. Cameras with larger lens apertures and larger pixels the point of view of an affordable VR180-camera. -
Caradoc of the North Wind Free
FREE CARADOC OF THE NORTH WIND PDF Allan Frewin Jones | 368 pages | 05 Apr 2012 | Hachette Children's Group | 9780340999417 | English | London, United Kingdom CARADOC OF THE NORTH WIND PDF As the war. Disaster strikes, and a valued friend suffers Caradoc of the North Wind devastating injury. Branwen sets off on a heroic journey towards destiny in an epic adventure of lovewar and revenge. Join Charlotte and Mia in this brilliant adventure full of princess sparkle and Christmas excitement! Chi ama i libri sceglie Kobo e inMondadori. The description is beautiful, but at times a bit too much, and sometimes at its worst the writing is hard to comprehend completely clearly. I find myself hoping vehemently for another book. It definitely allows the I read this in Caradoc of the North Wind sitting and could not put it down. Fair Wind to Widdershins. This author has published under several versions of his name, including Allan Jones, Frewin Jones, and A. Write a product review. Now he has stolen the feathers Caradoc of the North Wind Doodle, the weather-vane cockerel in charge of the weather. Jun 29, Katie rated it really liked it. Is the other warrior child, Arthur?? More than I thought I would, even. I really cafadoc want to know more, and off author is one that can really take you places. Join us by creating an account and start getting the best experience from our website! Jules Ember was raised hearing legends of wjnd ancient magic of the wicked Alchemist and the good Sorceress. Delivery and Returns see our delivery rates and policies thinking of returning an item? Mar 24, Valentina rated it really liked it. -
Proposal for a SIGGRAPH 2007 Course
Proposal for a SIGGRAPH 2008 Course: Computational Photography: Advanced Topics Online ID: courses_0040 1 Course Title Computational Photography 2 Category Image-based and 3D Photography techniques - other 3 Course Organizer Ramesh Raskar MIT-Media Lab (preferred) Mitsubishi Electric Research Labs (MERL) 20 Ames St., 201 Broadway Cambridge, MA 02139, USA Cambridge, MA 02139, USA Jack Tumblin EECS Dept, 3-320 Ford Design Center Northwestern University 2133 Sheridan Road Evanston, IL 60208, USA 4 Proposed Length 3.75 Hour 5 Proposed Presentation Venue Regular session room 6 Summary Statement Participants learn about the latest computational methods in digital imaging that overcome the traditional limitations of a camera and enable novel imaging applications. The course provides a practical guide to topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. 7 Names of Lecturers Paul DEBEVEC(University of Southern California, Institute for Creative Technologies (USC-ICT), USA)([email protected]) Ramesh RASKAR (Mitsubishi Electric Research Labs (MERL), USA) ([email protected]) Jack TUMBLIN (Northwestern University, USA) ([email protected]) 8 Course Abstract Abstract Computational photography combines plentiful computing, digital sensors, modern optics, many varieties of actuators, probes and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new interactive forms of photos that are partly snapshots and partly videos, performance capture and interchangeably relighting real and virtual characters are just some of the new applications emerging in Computational Photography.