CS488/688 Fall 2017 References
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Bibliography.Pdf
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. -
The Uses of Animation 1
The Uses of Animation 1 1 The Uses of Animation ANIMATION Animation is the process of making the illusion of motion and change by means of the rapid display of a sequence of static images that minimally differ from each other. The illusion—as in motion pictures in general—is thought to rely on the phi phenomenon. Animators are artists who specialize in the creation of animation. Animation can be recorded with either analogue media, a flip book, motion picture film, video tape,digital media, including formats with animated GIF, Flash animation and digital video. To display animation, a digital camera, computer, or projector are used along with new technologies that are produced. Animation creation methods include the traditional animation creation method and those involving stop motion animation of two and three-dimensional objects, paper cutouts, puppets and clay figures. Images are displayed in a rapid succession, usually 24, 25, 30, or 60 frames per second. THE MOST COMMON USES OF ANIMATION Cartoons The most common use of animation, and perhaps the origin of it, is cartoons. Cartoons appear all the time on television and the cinema and can be used for entertainment, advertising, 2 Aspects of Animation: Steps to Learn Animated Cartoons presentations and many more applications that are only limited by the imagination of the designer. The most important factor about making cartoons on a computer is reusability and flexibility. The system that will actually do the animation needs to be such that all the actions that are going to be performed can be repeated easily, without much fuss from the side of the animator. -
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. -
An Advanced Path Tracing Architecture for Movie Rendering
RenderMan: An Advanced Path Tracing Architecture for Movie Rendering PER CHRISTENSEN, JULIAN FONG, JONATHAN SHADE, WAYNE WOOTEN, BRENDEN SCHUBERT, ANDREW KENSLER, STEPHEN FRIEDMAN, CHARLIE KILPATRICK, CLIFF RAMSHAW, MARC BAN- NISTER, BRENTON RAYNER, JONATHAN BROUILLAT, and MAX LIANI, Pixar Animation Studios Fig. 1. Path-traced images rendered with RenderMan: Dory and Hank from Finding Dory (© 2016 Disney•Pixar). McQueen’s crash in Cars 3 (© 2017 Disney•Pixar). Shere Khan from Disney’s The Jungle Book (© 2016 Disney). A destroyer and the Death Star from Lucasfilm’s Rogue One: A Star Wars Story (© & ™ 2016 Lucasfilm Ltd. All rights reserved. Used under authorization.) Pixar’s RenderMan renderer is used to render all of Pixar’s films, and by many 1 INTRODUCTION film studios to render visual effects for live-action movies. RenderMan started Pixar’s movies and short films are all rendered with RenderMan. as a scanline renderer based on the Reyes algorithm, and was extended over The first computer-generated (CG) animated feature film, Toy Story, the years with ray tracing and several global illumination algorithms. was rendered with an early version of RenderMan in 1995. The most This paper describes the modern version of RenderMan, a new architec- ture for an extensible and programmable path tracer with many features recent Pixar movies – Finding Dory, Cars 3, and Coco – were rendered that are essential to handle the fiercely complex scenes in movie production. using RenderMan’s modern path tracing architecture. The two left Users can write their own materials using a bxdf interface, and their own images in Figure 1 show high-quality rendering of two challenging light transport algorithms using an integrator interface – or they can use the CG movie scenes with many bounces of specular reflections and materials and light transport algorithms provided with RenderMan. -
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. -
Deep Compositing Using Lie Algebras
Deep Compositing Using Lie Algebras TOM DUFF Pixar Animation Studios Deep compositing is an important practical tool in creating digital imagery, Deep images extend Porter-Duff compositing by storing multiple but there has been little theoretical analysis of the underlying mathematical values at each pixel with depth information to determine composit- operators. Motivated by finding a simple formulation of the merging oper- ing order. The OpenEXR deep image representation stores, for each ation on OpenEXR-style deep images, we show that the Porter-Duff over pixel, a list of nonoverlapping depth intervals, each containing a function is the operator of a Lie group. In its corresponding Lie algebra, the single pixel value [Kainz 2013]. An interval in a deep pixel can splitting and mixing functions that OpenEXR deep merging requires have a represent contributions either by hard surfaces if the interval has particularly simple form. Working in the Lie algebra, we present a novel, zero extent or by volumetric objects when the interval has nonzero simple proof of the uniqueness of the mixing function. extent. Displaying a single deep image requires compositing all the The Lie group structure has many more applications, including new, values in each deep pixel in depth order. When two deep images correct resampling algorithms for volumetric images with alpha channels, are merged, splitting and mixing of these intervals are necessary, as and a deep image compression technique that outperforms that of OpenEXR. described in the following paragraphs. 26 r Merging the pixels of two deep images requires that correspond- CCS Concepts: Computing methodologies → Antialiasing; Visibility; ing pixels of the two images be reconciled so that all overlapping Image processing; intervals are identical in extent. -
Loren Carpenter, Inventor and President of Cinematrix, Is a Pioneer in the Field of Computer Graphics
Loren Carpenter BIO February 1994 Loren Carpenter, inventor and President of Cinematrix, is a pioneer in the field of computer graphics. He received his formal education in computer science at the University of Washington in Seattle. From 1966 to 1980 he was employed by The Boeing Company in a variety of capacities, primarily software engineering and research. While there, he advanced the state of the art in image synthesis with now standard algorithms for synthesizing images of sculpted surfaces and fractal geometry. His 1980 film Vol Libre, the world’s first fractal movie, received much acclaim along with employment possibilities countrywide. He chose Lucasfilm. His fractal technique and others were usedGenesis in the sequence Starin Trek II: The Wrath of Khan. It was such a popular sequence that Paramount used it in most of the other Star Trek movies. The Lucasfilm computer division produced sequences for several movies:Star Trek II: The Wrath of Khan, Young Sherlock Holmes, andReturn of the Jedl, plus several animated short films. In 1986, the group spun off to form its own business, Pixar. Loren continues to be Senior Scientist for the company, solving interesting problems and generally improving the state of the art. Pixar is now producing the first completely computer animated feature length motion picture with the support of Disney, a logical step after winning the Academy Award for best animatedTin short Toy for in 1989. In 1993, Loren received a Scientific and Technical Academy Award for his fundamental contributions to the motion picture industry through the invention and development of the RenderMan image synthesis software system. -
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. -
Press Release
Press release CaixaForum Madrid From 21 March to 22 June 2014 Press release CaixaForum Madrid hosts the first presentation in Spain of a show devoted to the history of a studio that revolutionised the world of animated film “The art challenges the technology. Technology inspires the art.” That is how John Lasseter, Chief Creative Officer at Pixar Animation Studios, sums up the spirit of the US company that marked a turning-point in the film world with its innovations in computer animation. This is a medium that is at once extraordinarily liberating and extraordinarily challenging, since everything, down to the smallest detail, must be created from nothing. Pixar: 25 Years of Animation casts its spotlight on the challenges posed by computer animation, based on some of the most memorable films created by the studio. Taking three key elements in the creation of animated films –the characters, the stories and the worlds that are created– the exhibition reveals the entire production process, from initial idea to the creation of worlds full of sounds, textures, music and light. Pixar: 25 Years of Animation traces the company’s most outstanding technical and artistic achievements since its first shorts in the 1980s, whilst also enabling visitors to discover more about the production process behind the first 12 Pixar feature films through 402 pieces, including drawings, “colorscripts”, models, videos and installations. Pixar: 25 Years of Animation . Organised and produced by : Pixar Animation Studios in cooperation with ”la Caixa” Foundation. Curator : Elyse Klaidman, Director, Pixar University and Archive at Pixar Animation Studios. Place : CaixaForum Madrid (Paseo del Prado, 36).