Vision For Graphics ICCV 2005 Vision for Graphics Larry Zitnick, Sing Bing Kang, Rick Szeliski Interactive Visual Media Group Microsoft Research Steve.

Slides:



Advertisements
Similar presentations
Free-viewpoint Immersive Networked Experience February 2010.
Advertisements

Interactive Video Tours MSR Interactive Visual Media Group //msrweb/vision/IBR Rick Szeliski, Sing Bing Kang, Matt Uyttendaele, Simon Winder, Antonio Criminisi.
GrabCut Interactive Image (and Stereo) Segmentation Carsten Rother Vladimir Kolmogorov Andrew Blake Antonio Criminisi Geoffrey Cross [based on Siggraph.
EVENTS: INRIA Work Review Nov 18 th, Madrid.
Image-Based Modeling, Rendering, and Lighting
Computer Vision (CSE P 576)
A Survey of Real-time Soft Shadows Algorithms Speaker: Alvin Date: 2003/7/23 EUROGRAPHICS 2003 J.-M. Hasenfratz, M. Lapierre, N. Holzschuch and F.X. Sillion.
Optimal Illumination for Image and Video Relighting Francesc Moreno-Noguer Shree K. Nayar Peter N. Belhumeur Department of Computer Science – Columbia.
Wrap Up : Rendering and Image Processing Alexei Efros.
Advanced Computer Graphics (Fall 2010) CS 283, Lecture 16: Image-Based Rendering and Light Fields Ravi Ramamoorthi
Computational Photography
View interpolation from a single view 1. Render object 2. Convert Z-buffer to range image 3. Re-render from new viewpoint 4. Use depths to resolve overlaps.
High-Quality Video View Interpolation
Image-Based Rendering Produce a new image from real images. Combining images Interpolation More exotic methods.
Samuel W. Hasinoff Sing Bing Kang Richard Szeliski Interactive Visual Media Group Microsoft Research Dept. of Computer.
CSCE 641 Computer Graphics: Image-based Modeling Jinxiang Chai.
CSCE 641 Computer Graphics: Image-based Rendering (cont.) Jinxiang Chai.
Introduction to Computer Vision CS223B, Winter 2005.
Computational Photography Light Field Rendering Jinxiang Chai.
Using Photographs to Enhance Videos of a Static Scene Pravin Bhat 1, C. Lawrence Zitnick 2, Noah Snavely 1, Aseem Agarwala 3, Maneesh Agrawala 4, Michael.
CSCE 641: Computer Graphics Image-based Rendering Jinxiang Chai.
CS 563 Advanced Topics in Computer Graphics View Interpolation and Image Warping by Brad Goodwin Images in this presentation are used WITHOUT permission.
Project 1 artifact winners Project 2 questions Project 2 extra signup slots –Can take a second slot if you’d like Announcements.
Convergence of vision and graphics Jitendra Malik University of California at Berkeley Jitendra Malik University of California at Berkeley.
 Marc Levoy IBM / IBR “The study of image-based modeling and rendering is the study of sampled representations of geometry.”
View interpolation from a single view 1. Render object 2. Convert Z-buffer to range image 3. Re-render from new viewpoint 4. Use depths to resolve overlaps.
Wrap Up : Computational Photography Alexei Efros, CMU, Fall 2005 © Robert Brown.
The University of Ontario CS 4487/9587 Algorithms for Image Analysis n Web page: Announcements, assignments, code samples/libraries,
CSCE 641 Computer Graphics: Image-based Modeling Jinxiang Chai.
Noise Estimation from a Single Image Ce Liu William T. FreemanRichard Szeliski Sing Bing Kang.
Computer Vision in Graphics Production Adrian Hilton Visual Media Research Group Centre for Vision, Speech and Signal Processing University of Surrey
© 2006 Autodesk1 Image-processing Technologies for Digital Content Creation
Research & Innovation 1 An Industry Perspective on VVG Research Oliver Grau BBC Research & Innovation VVG SUMMER SCHOOL '07.
The Uncanny Valley in 3D Modeling Steve Seitz and Rick Szeliski BIRS Workshop on Computer Vision and the Internet August 31, 2009.
Integration Of CG & Live-Action For Cinematic Visual Effects by Amarnath Director, Octopus Media School.
Being There: Capturing and Experiencing a Sense of Place Richard Szeliski Microsoft Research Symposium on Computational Photography and Video.
Computational Photography and Videography Christian Theobalt and Ivo Ihrke Winter term 09/10.
Components of a computer vision system
Invitation to Computer Science 5th Edition
3D COMPUTER GRAPHICS IMD Chapter 1: 3D Computer Graphics Chapter 1: 1 Lecturer: Norhayati Mohd Amin.
Computer Graphics Psychophysics Heinrich H. Bülthoff Max-Planck-Institute for Biological Cybernetics Tübingen, Germany Heinrich H. Bülthoff Max-Planck-Institute.
Seminar on Media Technology Computer Vision Albert Alemany Font.
Photographic Realism, Transparency, and Perception Zsolt Bátori Budapest University of Technology and Economics Moholy-Nagy University of Art and Design.
Image-based rendering Michael F. Cohen Microsoft Research.
IMAGE SYNTHESIS 1 Image Synthesis Image synthesis operations create images from other images or non-image data Used when a desired image is either physically.
16421: Vision Sensors Lecture 7: High Dynamic Range Imaging Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 3:00pm.
Image stitching Digital Visual Effects Yung-Yu Chuang with slides by Richard Szeliski, Steve Seitz, Matthew Brown and Vaclav Hlavac.
Research Interests of Dr. Dennis J Bouvier Fall 2007.
Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-based Graphics with Global Illumination and High Dynamic Range Photography.
CPSC : Data-driven Computer Graphics Jinxiang Chai.
TELE IMMERSION AMAN BABBER
Computer Graphics Lecture 02 Fasih ur Rehman. Last Class Introduction to Computer Graphics Areas Application.
IMAGE SYNTHESIS 1 Image Synthesis Image synthesis operations create images from other images or non-image data Used when a desired image is either physically.
Image-Based Modeling of Complex Surfaces Todd Zickler DEAS, Harvard University.
CS559: Computer Graphics Lecture 36: Raytracing Li Zhang Spring 2008 Many Slides are from Hua Zhong at CUM, Paul Debevec at USC.
Video Textures Arno Schödl Richard Szeliski David Salesin Irfan Essa Microsoft Research, Georgia Tech.
Advanced Computer Graphics
CS262: Computer Vision (and Human-Computer Interaction)
Dynamic View Morphing performs view interpolation of dynamic scenes.
POSITION-CORRECTING TOOLS FOR 2D DIGITAL FABRICATION
Jun Shimamura, Naokazu Yokoya, Haruo Takemura and Kazumasa Yamazawa
Steps Towards the Convergence of Graphics, Vision, and Video
Journal of Vision. 2010;10(14):32. doi: / Figure Legend:
Sebastian Thrun, Stanford Rick Szeliski, Microsoft
Image Based Modeling and Rendering (PI: Malik)
Fully automated trimap generation for matting with Kinect
Rob Fergus Computer Vision
Professor Sebastian Thrun CAs: Dan Maynes-Aminzade and Mitul Saha
Computer Vision Computer vision attempts to construct meaningful and explicit descriptions of the world depicted in an image Using machines to Interpret!!!
Interactive media.
Presentation transcript:

Vision For Graphics ICCV 2005 Vision for Graphics Larry Zitnick, Sing Bing Kang, Rick Szeliski Interactive Visual Media Group Microsoft Research Steve Sullivan Industrial Light and Magic

Vision For Graphics ICCV 2005 Computer Graphics Image Output Model Synthetic Camera

Vision For Graphics ICCV 2005 Real Scene Computer Vision Real Cameras Model Output

Vision For Graphics ICCV 2005 Combined: Vision for Graphics Model Real Scene Real Cameras Image Output Synthetic Camera

Vision For Graphics ICCV 2005 What makes vision and graphics different? Graphics How photo-realistic? Vision How close to the ground truth?

Vision For Graphics ICCV 2005 What makes vision and graphics different? Graphics User interaction. Vision Fully automatic.

Vision For Graphics ICCV 2005 Agenda 5:00-6:00pm: Vision and special effects (Steve) 3:50-4:20pm: Video-based rendering (Rick) 3:30-3:50pm: Break 3:00-3:30pm: Image matting and inpainting (Rick) 2:00-3:00pm: Survey on image-based rendering and stereo (Sing Bing) 4:20-5:00pm: High-quality video interpolation (Larry)

Vision For Graphics ICCV 2005 Image-based rendering: Static and dynamic scenes Sing Bing Kang Microsoft Research Redmond, WA

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2024 SlidePlayer.com Inc. All rights reserved.