Image-Based Modeling and Rendering. Image-Based Modeling and Rendering. Final projects IBMR. What we have learnt so far. What IBMR is about

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Image-Based Modeling and Rendering. Image-Based Modeling and Rendering. Final projects IBMR. What we have learnt so far. What IBMR is about"

Transcription

1 Image-Based Modeling and Rendering Image-Based Modeling and Rendering MIT EECS Frédo Durand and Seth Teller 1 Some slides courtesy of Leonard McMillan, Wojciech Matusik, Byong Mok Oh, Max Chen 2 Final projects Final report due tomorrow Friday at 5pm Next Tuesday: Best project presentation in class IBMR Image-Based Modeling and Rendering Reuse images to generate new views Use images to create 3D models New area of computer graphics (mid nineties) Lot of research at MIT 3 4 What we have learnt so far Graphics is about producing images from a scene description Geometry + reflectance property + light From continuous description to a discrete image What IBMR is about Focus on discrete descriptions Forget (mostly) about geometry IBMR is about samples cf. lecture on sampling and antialiasing 5 6

2 Motivations Geometry is costly Triangles become tiny Triangles are not good for complex objects, e.g. plants Photos are photorealistic Well, we could argue about that another time Modeling is tedious IBR Principles Consider images as a collection of rays, rather than a collection of pixels. 7 8 The Plenoptic Function Complete view for each point of space 3D (for position) * 2D (for view) *1D for wavelength * 1D for time Image-based rendering is about signal reconstruction rather than physical simulation. 9 5-D for static scenes in general 4-D in empty regions of static scenes 6-D for dynamic scenes 10 Geometry vs. sample Geometry exploits the structure of the scene Compression (information theory point of view) IBMR is brute force Closer to final images Simpler to render More costly to store Harder to manipulate, create, edit To decrease dimensionality Add geometry for structure Forget some visual phenomena We already know some IBR Texture mapping! 11 12

3 Overview Questions? Image-based rendering Various dimensionalities Various structure Image-based Modeling Image-Based Modeling and Editing QuickTime VR & Panoramas 2D reconstruction only: 1 single complete view Video 3D slice See Light field and Lumigraph 4D representation Plane of cameras 2-plane parameterization Lines in space have 4D 17 From [Gortler et al 96] 18

4 2-plane parameterization Sample this 4D space of light rays What do we miss in the plenoptic function? Light field reconstruction Compute an image similar to ray casting Simple lookup Can be optimized Light field Light-field/Lumigraph slice Collection of rays Collection of images Dynamic Reparameterization Demo Structure Structure Which ray is best? Which ray is best? s,t u,v s,t u,v New ray New ray 23 Geometry matters! 24

5 Depth correction: Lumigraph Without depth correction With depth correction From [Gortler et al. 96] 25 Light Field Acquisition Motion Platforms Precise positioning Calibrated digital camera Expensive (> $10K) Very Slow (~20 mins) Light field cameras Less precise Calibration per aperture Inexpensive (~ $100) Slow (> 3 mins) 26 LF Rendering Questions? Advantages Simple/Fast rendering algorithm View-dependent shading High-quality reconstructions Disadvantages Huge memory footprint (compression) Incompatible with traditional graphics Images with Depth [Chen & Williams 1993, McMillan & Bishop 1995] 2D representation (image) + depth per pixel View warping to reproject pixels Tradeoff between dimensionality and geometric information View warping [Chen & Williams 1993, McMillan & Bishop 1995] Reprojects pixels with depth 2 Types of problems Disocclusion Undersampling Undersampling can be alleviated using splatting Instead of displaying a pixel, display a splat (ellipse) Color Depth 29 30

6 Layered Depth Images (LDI) Same as Images with depth, but multiple samples in depth for one (x,y) pixel Solves many disocclusion artifacts Acquiring Depth Images Economical laser scanners Demo 31 Images and video courtesy of the University of North Carolina at Chapel Hill, Office of the Future Project, Wei-Chao Chen, Henry Fuchs, Lars Nyland, Herman Towles and Greg Welch. 32 Visual Hulls What is a Visual Hull? Intersection of all silhouette cones Why Use a Visual Hull? Finding silhouettes is simple and robust Blue-screen methods Image differenceing Contains actual object Can seed more sophisticated methods Acquisition Several cameras with overlapping views Geometric calibration Photometric calibration Synchronization background + foreground background - = foreground 35 36

7 Representing a Visual Hull Geometric Model + Know how to render Calculating intersections isn t robust High polygonal complexity Volumetric Model + Easy to compute Expensive storage Result seldom predicts inputs Image-based Visual Hulls Volume-like Self-consistent Discretediscretecontinuous Shading Visual Hulls Results View-dependent illumination Visibility 39 A range of different virtual viewpoints of a visual hull computed from four cameras in real-time. Top images show depth maps and bottom images show shaded visual hulls. The background is a textured polygonal model. 40 Results Video Summary Plenoptic function is mostly 5D Images and panoramas are 2D Video is 3D Light fields are 4D Structure can help Images with depth Visual hull 41 42

8 Questions? Overview Image-based rendering Various dimensionalities Various structure Image-based Modeling Image-Based Modeling and Editing Image-based Modeling From a set of 2D images Build a 3D polygonal model Using vision techniques & user intervention Polygonal Representation Modeling and Rendering Architecture from Photographs [Debevec et al. 1996] From [Debevec et al. 1996] 45 From [Debevec et al. 1996] 46 Image-based modeling Video Write down the equations Optimize Camera coordinate system Edge location From [Debevec et al. 1996] 47 48

9 Questions? Overview Image-based rendering Various dimensionalities Various structure Image-based Modeling Image-Based Modeling and Editing Graphics Durand Image-based modeling & editing Image-Based Representation Images with depth per pixel [Chen 93] Layers Joint work with Oh, Chen & Dorsey Create an Image-Based representation Editing of sampled representation Our context: Single image as input Color Input image New viewpoint Depth Layers Relighting 51 Workflow 52 Depth Assignment Tool Tool that assigns or modifies the depth of pixels Similar to tools of 2D photo editing But on the depth channel Apply depth Input image Segment Edit, relight MIT EECS 6.837, Teller and Durand Clone brush holes 53 Color channel Depth channel Side view 54 9

10 Depth Assignment and Selection Going Beyond Painting Arbitrary selection/segmentation restricts the affected pixels Painting absolute depth is hard Hybrid geometric tools But still pixel based (flexible, use of selection) Geometry is temporary Selection area Color channel Depth channel Side view Initial image (side view) Correlate Update Read &6.837, align depth z-buffer 3Dand data geometry MIT EECS Teller Durand Ground Plane Tool Vertical Tool The ground plane is easy to infer (horizon) Will be used as a reference Uses ground plane as reference Draw contact between ground and object in image vertical shape user input user input user input Reference view Depth channel ground plane Side view user input Reference view Depth channel 57 Other Geometric Primitives 58 Organic Shapes Sphere, cylinder, box, pyramid, etc. Possible snapping to constrain verticality Level set method [Wil98,IMT99] Distant depth at boundary, closer depth towards center Layer Side view 59 Depth channel 60 10

11 Generic Geometry Tool 3D template User defined point correspondences 3D pose optimization Refinement through 2D morphing Depth Painting & Chiseling Paint on depth channel [Kan98] Relative or absolute Local smoothing, sharpening User defined point correspondences Optimized pose Side view 61 Layer Depth channel 62 Refined Example Clone brushing Apply depth Input image Segment Clone brush holes Coarse depth Refined depth 63 MIT EECS 6.837, Teller and Edit, Durand relight 64 2D Clone Brush 2D Clone Brush Copy via brush interface Copy via brush interface source pixel destination pixel source pixel destination pixel 65 66

12 2D Clone Brush 2D Clone Brush Copy via brush interface Copy via brush interface source pixel destination pixel source pixel destination pixel D Clone Brush Copy via brush interface Limitations of 2D Clone Brushing Distortions due to foreshortening and surface orientation source pixel destination pixel Coping with Distortions Examples We have depth information Minimize distortion v Non-distorted parameterization Iterative optimization (u,v) u v (u,v) u 71 72

13 Results Hotel Lobby Results Dali Tuesday Best project presentations Images vs. geometry Photos Film Texture mapping Environment mapping 75 76

A Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India

A Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India A Review of Image- based Rendering Techniques Nisha 1, Vijaya Goel 2 1 Department of computer science, University of Delhi, Delhi, India Keshav Mahavidyalaya, University of Delhi, Delhi, India Abstract

More information

Hybrid Rendering for Collaborative, Immersive Virtual Environments

Hybrid Rendering for Collaborative, Immersive Virtual Environments Hybrid Rendering for Collaborative, Immersive Virtual Environments Stephan Würmlin wuermlin@inf.ethz.ch Outline! Rendering techniques GBR, IBR and HR! From images to models! Novel view generation! Putting

More information

Image-Based Rendering. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen

Image-Based Rendering. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen Image-Based Rendering Image-Based Rendering What is it? Still a difficult question to answer Uses images (photometric( info) as key component of model representation What s Good about IBR Model acquisition

More information

Image-Based Rendering. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen

Image-Based Rendering. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen Image-Based Rendering Image-Based Rendering What is it? Still a difficult question to answer Uses images (photometric( info) as key component of model representation What s Good about IBR Model acquisition

More information

Image-Based Rendering. Image-Based Rendering

Image-Based Rendering. Image-Based Rendering Image-Based Rendering Image-Based Rendering What is it? Still a difficult question to answer Uses images (photometric info) as key component of model representation 1 What s Good about IBR Model acquisition

More information

Image-based modeling (IBM) and image-based rendering (IBR)

Image-based modeling (IBM) and image-based rendering (IBR) Image-based modeling (IBM) and image-based rendering (IBR) CS 248 - Introduction to Computer Graphics Autumn quarter, 2005 Slides for December 8 lecture The graphics pipeline modeling animation rendering

More information

Image-Based Rendering

Image-Based Rendering Image-Based Rendering COS 526, Fall 2016 Thomas Funkhouser Acknowledgments: Dan Aliaga, Marc Levoy, Szymon Rusinkiewicz What is Image-Based Rendering? Definition 1: the use of photographic imagery to overcome

More information

Image-Based Modeling and Rendering

Image-Based Modeling and Rendering Traditional Computer Graphics Image-Based Modeling and Rendering Thomas Funkhouser Princeton University COS 426 Guest Lecture Spring 2003 How would you model and render this scene? (Jensen) How about this

More information

Multi-view stereo. Many slides adapted from S. Seitz

Multi-view stereo. Many slides adapted from S. Seitz Multi-view stereo Many slides adapted from S. Seitz Beyond two-view stereo The third eye can be used for verification Multiple-baseline stereo Pick a reference image, and slide the corresponding window

More information

Image Base Rendering: An Introduction

Image Base Rendering: An Introduction Image Base Rendering: An Introduction Cliff Lindsay CS563 Spring 03, WPI 1. Introduction Up to this point, we have focused on showing 3D objects in the form of polygons. This is not the only approach to

More information

Image Based Rendering

Image Based Rendering Image Based Rendering an overview Photographs We have tools that acquire and tools that display photographs at a convincing quality level 2 1 3 4 2 5 6 3 7 8 4 9 10 5 Photographs We have tools that acquire

More information

CSE528 Computer Graphics: Theory, Algorithms, and Applications

CSE528 Computer Graphics: Theory, Algorithms, and Applications CSE528 Computer Graphics: Theory, Algorithms, and Applications Hong Qin State University of New York at Stony Brook (Stony Brook University) Stony Brook, New York 11794--4400 Tel: (631)632-8450; Fax: (631)632-8334

More information

But, vision technology falls short. and so does graphics. Image Based Rendering. Ray. Constant radiance. time is fixed. 3D position 2D direction

But, vision technology falls short. and so does graphics. Image Based Rendering. Ray. Constant radiance. time is fixed. 3D position 2D direction Computer Graphics -based rendering Output Michael F. Cohen Microsoft Research Synthetic Camera Model Computer Vision Combined Output Output Model Real Scene Synthetic Camera Model Real Cameras Real Scene

More information

The Light Field and Image-Based Rendering

The Light Field and Image-Based Rendering Lecture 11: The Light Field and Image-Based Rendering Visual Computing Systems Demo (movie) Royal Palace: Madrid, Spain Image-based rendering (IBR) So far in course: rendering = synthesizing an image from

More information

Efficient View-Dependent Sampling of Visual Hulls

Efficient View-Dependent Sampling of Visual Hulls Efficient View-Dependent Sampling of Visual Hulls Wojciech Matusik Chris Buehler Leonard McMillan Computer Graphics Group MIT Laboratory for Computer Science Cambridge, MA 02141 Abstract In this paper

More information

Image-Based Rendering and Light Fields

Image-Based Rendering and Light Fields CS194-13: Advanced Computer Graphics Lecture #9 Image-Based Rendering University of California Berkeley Image-Based Rendering and Light Fields Lecture #9: Wednesday, September 30th 2009 Lecturer: Ravi

More information

A Warping-based Refinement of Lumigraphs

A Warping-based Refinement of Lumigraphs A Warping-based Refinement of Lumigraphs Wolfgang Heidrich, Hartmut Schirmacher, Hendrik Kück, Hans-Peter Seidel Computer Graphics Group University of Erlangen heidrich,schirmacher,hkkueck,seidel@immd9.informatik.uni-erlangen.de

More information

Jingyi Yu CISC 849. Department of Computer and Information Science

Jingyi Yu CISC 849. Department of Computer and Information Science Digital Photography and Videos Jingyi Yu CISC 849 Light Fields, Lumigraph, and Image-based Rendering Pinhole Camera A camera captures a set of rays A pinhole camera captures a set of rays passing through

More information

Image Processing: 3D Image Warping. Spatial Transformations. Spatial Transformations. Affine Transformations. Affine Transformations CS334

Image Processing: 3D Image Warping. Spatial Transformations. Spatial Transformations. Affine Transformations. Affine Transformations CS334 Image Warping Image rocessing: 3D Image Warping We can divide image arping into Simple spatial transformations (no per-pixel depth information) Full 3D transformations (needs per-pixel depth information)

More information

Lecture 15: Image-Based Rendering and the Light Field. Kayvon Fatahalian CMU : Graphics and Imaging Architectures (Fall 2011)

Lecture 15: Image-Based Rendering and the Light Field. Kayvon Fatahalian CMU : Graphics and Imaging Architectures (Fall 2011) Lecture 15: Image-Based Rendering and the Light Field Kayvon Fatahalian CMU 15-869: Graphics and Imaging Architectures (Fall 2011) Demo (movie) Royal Palace: Madrid, Spain Image-based rendering (IBR) So

More information

Image-Based Rendering using Image-Warping Motivation and Background

Image-Based Rendering using Image-Warping Motivation and Background Image-Based Rendering using Image-Warping Motivation and Background Leonard McMillan LCS Computer Graphics Group MIT The field of three-dimensional computer graphics has long focused on the problem of

More information

Rasterization. MIT EECS Frédo Durand and Barb Cutler. MIT EECS 6.837, Cutler and Durand 1

Rasterization. MIT EECS Frédo Durand and Barb Cutler. MIT EECS 6.837, Cutler and Durand 1 Rasterization MIT EECS 6.837 Frédo Durand and Barb Cutler MIT EECS 6.837, Cutler and Durand 1 Final projects Rest of semester Weekly meetings with TAs Office hours on appointment This week, with TAs Refine

More information

Computational Photography

Computational Photography Computational Photography Photography and Imaging Michael S. Brown Brown - 1 Part 1 Overview Photography Preliminaries Traditional Film Imaging (Camera) Part 2 General Imaging 5D Plenoptic Function (McMillan)

More information

A million pixels, a million polygons. Which is heavier? François X. Sillion. imagis* Grenoble, France

A million pixels, a million polygons. Which is heavier? François X. Sillion. imagis* Grenoble, France A million pixels, a million polygons. Which is heavier? François X. Sillion imagis* Grenoble, France *A joint research project of CNRS, INRIA, INPG and UJF MAGIS Why this question? Evolution of processing

More information

CS 431/636 Advanced Rendering Techniques

CS 431/636 Advanced Rendering Techniques CS 431/636 Advanced Rendering Techniques Dr. David Breen Matheson 308 Thursday 6PM 8:50PM Presentation 7 5/23/06 Questions from Last Time? Hall Shading Model Shadows Reflections Refractions Slide Credits

More information

CSc Topics in Computer Graphics 3D Photography

CSc Topics in Computer Graphics 3D Photography CSc 83010 Topics in Computer Graphics 3D Photography Tuesdays 11:45-1:45 1:45 Room 3305 Ioannis Stamos istamos@hunter.cuny.edu Office: 1090F, Hunter North (Entrance at 69 th bw/ / Park and Lexington Avenues)

More information

High-Quality Interactive Lumigraph Rendering Through Warping

High-Quality Interactive Lumigraph Rendering Through Warping High-Quality Interactive Lumigraph Rendering Through Warping Hartmut Schirmacher, Wolfgang Heidrich, and Hans-Peter Seidel Max-Planck-Institut für Informatik Saarbrücken, Germany http://www.mpi-sb.mpg.de

More information

Image-Based Modeling and Rendering

Image-Based Modeling and Rendering Image-Based Modeling and Rendering Richard Szeliski Microsoft Research IPAM Graduate Summer School: Computer Vision July 26, 2013 How far have we come? Light Fields / Lumigraph - 1996 Richard Szeliski

More information

Image or Object? Is this real?

Image or Object? Is this real? Image or Object? Michael F. Cohen Microsoft Is this real? Photo by Patrick Jennings (patrick@synaptic.bc.ca), Copyright 1995, 96, 97 Whistler B. C. Canada Modeling, Rendering, and Lighting 1 A mental model?

More information

Modeling Light. On Simulating the Visual Experience

Modeling Light. On Simulating the Visual Experience Modeling Light 15-463: Rendering and Image Processing Alexei Efros On Simulating the Visual Experience Just feed the eyes the right data No one will know the difference! Philosophy: Ancient question: Does

More information

Visibility. Tom Funkhouser COS 526, Fall Slides mostly by Frédo Durand

Visibility. Tom Funkhouser COS 526, Fall Slides mostly by Frédo Durand Visibility Tom Funkhouser COS 526, Fall 2016 Slides mostly by Frédo Durand Visibility Compute which part of scene can be seen Visibility Compute which part of scene can be seen (i.e., line segment from

More information

Texture Mapping II. Light maps Environment Maps Projective Textures Bump Maps Displacement Maps Solid Textures Mipmaps Shadows 1. 7.

Texture Mapping II. Light maps Environment Maps Projective Textures Bump Maps Displacement Maps Solid Textures Mipmaps Shadows 1. 7. Texture Mapping II Light maps Environment Maps Projective Textures Bump Maps Displacement Maps Solid Textures Mipmaps Shadows 1 Light Maps Simulates the effect of a local light source + = Can be pre-computed

More information

Multiple View Geometry

Multiple View Geometry Multiple View Geometry Martin Quinn with a lot of slides stolen from Steve Seitz and Jianbo Shi 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 Our Goal The Plenoptic Function P(θ,φ,λ,t,V

More information

Morphable 3D-Mosaics: a Hybrid Framework for Photorealistic Walkthroughs of Large Natural Environments

Morphable 3D-Mosaics: a Hybrid Framework for Photorealistic Walkthroughs of Large Natural Environments Morphable 3D-Mosaics: a Hybrid Framework for Photorealistic Walkthroughs of Large Natural Environments Nikos Komodakis and Georgios Tziritas Computer Science Department, University of Crete E-mails: {komod,

More information

More and More on Light Fields. Last Lecture

More and More on Light Fields. Last Lecture More and More on Light Fields Topics in Image-Based Modeling and Rendering CSE291 J00 Lecture 4 Last Lecture Re-review with emphasis on radiometry Mosaics & Quicktime VR The Plenoptic function The main

More information

The Traditional Graphics Pipeline

The Traditional Graphics Pipeline Final Projects Proposals due Thursday 4/8 Proposed project summary At least 3 related papers (read & summarized) Description of series of test cases Timeline & initial task assignment The Traditional Graphics

More information

Image Processing: Motivation Rendering from Images. Related Work. Overview. Image Morphing Examples. Overview. View and Image Morphing CS334

Image Processing: Motivation Rendering from Images. Related Work. Overview. Image Morphing Examples. Overview. View and Image Morphing CS334 Motivation Rendering from Images Image rocessing: View and CS334 Given left image right image Create intermediate images simulates camera movement [Seitz96] Related Work anoramas ([Chen95/QuicktimeVR],

More information

Volumetric Scene Reconstruction from Multiple Views

Volumetric Scene Reconstruction from Multiple Views Volumetric Scene Reconstruction from Multiple Views Chuck Dyer University of Wisconsin dyer@cs cs.wisc.edu www.cs cs.wisc.edu/~dyer Image-Based Scene Reconstruction Goal Automatic construction of photo-realistic

More information

Modeling Light. Michal Havlik : Computational Photography Alexei Efros, CMU, Fall 2007

Modeling Light. Michal Havlik : Computational Photography Alexei Efros, CMU, Fall 2007 Modeling Light Michal Havlik 15-463: Computational Photography Alexei Efros, CMU, Fall 2007 The Plenoptic Function Figure by Leonard McMillan Q: What is the set of all things that we can ever see? A: The

More information

CSL 859: Advanced Computer Graphics. Dept of Computer Sc. & Engg. IIT Delhi

CSL 859: Advanced Computer Graphics. Dept of Computer Sc. & Engg. IIT Delhi CSL 859: Advanced Computer Graphics Dept of Computer Sc. & Engg. IIT Delhi Point Based Representation Point sampling of Surface Mesh construction, or Mesh-less Often come from laser scanning Or even natural

More information

Shape from Silhouettes II

Shape from Silhouettes II Shape from Silhouettes II Guido Gerig CS 6320, S2013 (slides modified from Marc Pollefeys UNC Chapel Hill, some of the figures and slides are adapted from M. Pollefeys, J.S. Franco, J. Matusik s presentations,

More information

Real-time Generation and Presentation of View-dependent Binocular Stereo Images Using a Sequence of Omnidirectional Images

Real-time Generation and Presentation of View-dependent Binocular Stereo Images Using a Sequence of Omnidirectional Images Real-time Generation and Presentation of View-dependent Binocular Stereo Images Using a Sequence of Omnidirectional Images Abstract This paper presents a new method to generate and present arbitrarily

More information

Shape from Silhouettes I

Shape from Silhouettes I Shape from Silhouettes I Guido Gerig CS 6320, Spring 2015 Credits: Marc Pollefeys, UNC Chapel Hill, some of the figures and slides are also adapted from J.S. Franco, J. Matusik s presentations, and referenced

More information

Multi-View 3D-Reconstruction

Multi-View 3D-Reconstruction Multi-View 3D-Reconstruction Cedric Cagniart Computer Aided Medical Procedures (CAMP) Technische Universität München, Germany 1 Problem Statement Given several calibrated views of an object... can we automatically

More information

Polyhedral Visual Hulls for Real-Time Rendering

Polyhedral Visual Hulls for Real-Time Rendering Polyhedral Visual Hulls for Real-Time Rendering Wojciech Matusik Chris Buehler Leonard McMillan MIT Laboratory for Computer Science Abstract. We present new algorithms for creating and rendering visual

More information

Shape from Silhouettes I

Shape from Silhouettes I Shape from Silhouettes I Guido Gerig CS 6320, Spring 2013 Credits: Marc Pollefeys, UNC Chapel Hill, some of the figures and slides are also adapted from J.S. Franco, J. Matusik s presentations, and referenced

More information

CS 684 Fall 2005 Image-based Modeling and Rendering. Ruigang Yang

CS 684 Fall 2005 Image-based Modeling and Rendering. Ruigang Yang CS 684 Fall 2005 Image-based Modeling and Rendering Ruigang Yang Administrivia Classes: Monday and Wednesday, 4:00-5:15 PM Instructor: Ruigang Yang ryang@cs.uky.edu Office Hour: Robotics 514D, MW 1500-1600

More information

The Traditional Graphics Pipeline

The Traditional Graphics Pipeline Last Time? The Traditional Graphics Pipeline Participating Media Measuring BRDFs 3D Digitizing & Scattering BSSRDFs Monte Carlo Simulation Dipole Approximation Today Ray Casting / Tracing Advantages? Ray

More information

Photorealism vs. Non-Photorealism in Computer Graphics

Photorealism vs. Non-Photorealism in Computer Graphics The Art and Science of Depiction Photorealism vs. Non-Photorealism in Computer Graphics Fredo Durand MIT- Lab for Computer Science Global illumination How to take into account all light inter-reflections

More information

MIT EECS 6.837, Teller and Durand 1

MIT EECS 6.837, Teller and Durand 1 MIT EECS 6.837, Teller and Durand 1 Clipping MIT EECS 6.837 Frédo Durand and Seth Teller Some slides and images courtesy of Leonard McMillan MIT EECS 6.837, Teller and Durand 2 Administrative Assignment

More information

The Traditional Graphics Pipeline

The Traditional Graphics Pipeline Last Time? The Traditional Graphics Pipeline Reading for Today A Practical Model for Subsurface Light Transport, Jensen, Marschner, Levoy, & Hanrahan, SIGGRAPH 2001 Participating Media Measuring BRDFs

More information

Some Thoughts on Visibility

Some Thoughts on Visibility Some Thoughts on Visibility Frédo Durand MIT Lab for Computer Science Visibility is hot! 4 papers at Siggraph 4 papers at the EG rendering workshop A wonderful dedicated workshop in Corsica! A big industrial

More information

Point based Rendering

Point based Rendering Point based Rendering CS535 Daniel Aliaga Current Standards Traditionally, graphics has worked with triangles as the rendering primitive Triangles are really just the lowest common denominator for surfaces

More information

A million pixels, a million polygons: which is heavier?

A million pixels, a million polygons: which is heavier? A million pixels, a million polygons: which is heavier? François X. Sillion To cite this version: François X. Sillion. A million pixels, a million polygons: which is heavier?. Eurographics 97, Sep 1997,

More information

Surface Rendering. Surface Rendering

Surface Rendering. Surface Rendering Surface Rendering Surface Rendering Introduce Mapping Methods - Texture Mapping - Environmental Mapping - Bump Mapping Go over strategies for - Forward vs backward mapping 2 1 The Limits of Geometric Modeling

More information

Multi-View Stereo for Static and Dynamic Scenes

Multi-View Stereo for Static and Dynamic Scenes Multi-View Stereo for Static and Dynamic Scenes Wolfgang Burgard Jan 6, 2010 Main references Yasutaka Furukawa and Jean Ponce, Accurate, Dense and Robust Multi-View Stereopsis, 2007 C.L. Zitnick, S.B.

More information

Final projects. Rasterization. The Graphics Pipeline. Illumination (Shading) (Lighting) Viewing Transformation. Rest of semester. This week, with TAs

Final projects. Rasterization. The Graphics Pipeline. Illumination (Shading) (Lighting) Viewing Transformation. Rest of semester. This week, with TAs Rasterization MIT EECS 6.837 Frédo Durand and Barb Cutler MIT EECS 6.837, Cutler and Durand Final projects Rest of semester Weekly meetings with TAs Office hours on appointment This week, with TAs Refine

More information

3-D Shape Reconstruction from Light Fields Using Voxel Back-Projection

3-D Shape Reconstruction from Light Fields Using Voxel Back-Projection 3-D Shape Reconstruction from Light Fields Using Voxel Back-Projection Peter Eisert, Eckehard Steinbach, and Bernd Girod Telecommunications Laboratory, University of Erlangen-Nuremberg Cauerstrasse 7,

More information

Texture. Texture Mapping. Texture Mapping. CS 475 / CS 675 Computer Graphics. Lecture 11 : Texture

Texture. Texture Mapping. Texture Mapping. CS 475 / CS 675 Computer Graphics. Lecture 11 : Texture Texture CS 475 / CS 675 Computer Graphics Add surface detail Paste a photograph over a surface to provide detail. Texture can change surface colour or modulate surface colour. Lecture 11 : Texture http://en.wikipedia.org/wiki/uv_mapping

More information

CS 475 / CS 675 Computer Graphics. Lecture 11 : Texture

CS 475 / CS 675 Computer Graphics. Lecture 11 : Texture CS 475 / CS 675 Computer Graphics Lecture 11 : Texture Texture Add surface detail Paste a photograph over a surface to provide detail. Texture can change surface colour or modulate surface colour. http://en.wikipedia.org/wiki/uv_mapping

More information

Acquisition and Visualization of Colored 3D Objects

Acquisition and Visualization of Colored 3D Objects Acquisition and Visualization of Colored 3D Objects Kari Pulli Stanford University Stanford, CA, U.S.A kapu@cs.stanford.edu Habib Abi-Rached, Tom Duchamp, Linda G. Shapiro and Werner Stuetzle University

More information

Fast Computation of Generalized Voronoi Diagrams Using Graphics Hardware

Fast Computation of Generalized Voronoi Diagrams Using Graphics Hardware Fast Computation of Generalized Voronoi Diagrams Using Graphics Hardware paper by Kennet E. Hoff et al. (University of North Carolina at Chapel Hill) presented by Daniel Emmenegger GDV-Seminar ETH Zürich,

More information

Depth. Common Classification Tasks. Example: AlexNet. Another Example: Inception. Another Example: Inception. Depth

Depth. Common Classification Tasks. Example: AlexNet. Another Example: Inception. Another Example: Inception. Depth Common Classification Tasks Recognition of individual objects/faces Analyze object-specific features (e.g., key points) Train with images from different viewing angles Recognition of object classes Analyze

More information

Lecture 17: Shadows. Projects. Why Shadows? Shadows. Using the Shadow Map. Shadow Maps. Proposals due today. I will mail out comments

Lecture 17: Shadows. Projects. Why Shadows? Shadows. Using the Shadow Map. Shadow Maps. Proposals due today. I will mail out comments Projects Lecture 17: Shadows Proposals due today I will mail out comments Fall 2004 Kavita Bala Computer Science Cornell University Grading HW 1: will email comments asap Why Shadows? Crucial for spatial

More information

Advanced Ray Tracing

Advanced Ray Tracing Advanced Ray Tracing Thanks to Fredo Durand and Barb Cutler The Ray Tree Ni surface normal Ri reflected ray Li shadow ray Ti transmitted (refracted) ray 51 MIT EECS 6.837, Cutler and Durand 1 Ray Tree

More information

Luxo Jr. Plan. Team. Movies. Why Computer Graphics? Introduction to Computer Graphics. Pixar Animation Studios, 1986 Director: John Lasseter

Luxo Jr. Plan. Team. Movies. Why Computer Graphics? Introduction to Computer Graphics. Pixar Animation Studios, 1986 Director: John Lasseter Luxo Jr 6.837 Introduction to Computer Graphics Pixar Animation Studios, 1986 Director: John Lasseter 2 Plan Introduction of the semester Administrivia Iterated Function Systems (fractals) 3 Team Lecturers

More information

Real-Time Shadows. Last Time? Today. Why are Shadows Important? Shadows as a Depth Cue. For Intuition about Scene Lighting

Real-Time Shadows. Last Time? Today. Why are Shadows Important? Shadows as a Depth Cue. For Intuition about Scene Lighting Last Time? Real-Time Shadows Today Why are Shadows Important? Shadows & Soft Shadows in Ray Tracing Planar Shadows Projective Texture Shadows Shadow Maps Shadow Volumes Why are Shadows Important? Depth

More information

Real-Time Shadows. MIT EECS 6.837, Durand and Cutler

Real-Time Shadows. MIT EECS 6.837, Durand and Cutler Real-Time Shadows Last Time? The graphics pipeline Clipping & rasterization of polygons Visibility the depth buffer (z-buffer) Schedule Quiz 2: Thursday November 20 th, in class (two weeks from Thursday)

More information

Algorithms for Image-Based Rendering with an Application to Driving Simulation

Algorithms for Image-Based Rendering with an Application to Driving Simulation Algorithms for Image-Based Rendering with an Application to Driving Simulation George Drettakis GRAPHDECO/Inria Sophia Antipolis, Université Côte d Azur http://team.inria.fr/graphdeco Graphics for Driving

More information

A Thin-Client Approach for Porting OpenGL Applications to Pocket PC s

A Thin-Client Approach for Porting OpenGL Applications to Pocket PC s A Thin-Client Approach for Porting OpenGL Applications to Pocket PC s Zhe-Yu Lin Shyh-Haur Ger Yung-Feng Chiu Chun-Fa Chang Department of Computer Science National Tsing Hua University Abstract The display

More information

Lecture 16: Computer Vision

Lecture 16: Computer Vision CS4442/9542b: Artificial Intelligence II Prof. Olga Veksler Lecture 16: Computer Vision Motion Slides are from Steve Seitz (UW), David Jacobs (UMD) Outline Motion Estimation Motion Field Optical Flow Field

More information

Lecture 16: Computer Vision

Lecture 16: Computer Vision CS442/542b: Artificial ntelligence Prof. Olga Veksler Lecture 16: Computer Vision Motion Slides are from Steve Seitz (UW), David Jacobs (UMD) Outline Motion Estimation Motion Field Optical Flow Field Methods

More information

graphics pipeline computer graphics graphics pipeline 2009 fabio pellacini 1

graphics pipeline computer graphics graphics pipeline 2009 fabio pellacini 1 graphics pipeline computer graphics graphics pipeline 2009 fabio pellacini 1 graphics pipeline sequence of operations to generate an image using object-order processing primitives processed one-at-a-time

More information

Pipeline Operations. CS 4620 Lecture 10

Pipeline Operations. CS 4620 Lecture 10 Pipeline Operations CS 4620 Lecture 10 2008 Steve Marschner 1 Hidden surface elimination Goal is to figure out which color to make the pixels based on what s in front of what. Hidden surface elimination

More information

Last Time. Reading for Today: Graphics Pipeline. Clipping. Rasterization

Last Time. Reading for Today: Graphics Pipeline. Clipping. Rasterization Last Time Modeling Transformations Illumination (Shading) Real-Time Shadows Viewing Transformation (Perspective / Orthographic) Clipping Projection (to Screen Space) Scan Conversion (Rasterization) Visibility

More information

graphics pipeline computer graphics graphics pipeline 2009 fabio pellacini 1

graphics pipeline computer graphics graphics pipeline 2009 fabio pellacini 1 graphics pipeline computer graphics graphics pipeline 2009 fabio pellacini 1 graphics pipeline sequence of operations to generate an image using object-order processing primitives processed one-at-a-time

More information

Last Time. Why are Shadows Important? Today. Graphics Pipeline. Clipping. Rasterization. Why are Shadows Important?

Last Time. Why are Shadows Important? Today. Graphics Pipeline. Clipping. Rasterization. Why are Shadows Important? Last Time Modeling Transformations Illumination (Shading) Real-Time Shadows Viewing Transformation (Perspective / Orthographic) Clipping Projection (to Screen Space) Graphics Pipeline Clipping Rasterization

More information

A Survey and Classification of Real Time Rendering Methods

A Survey and Classification of Real Time Rendering Methods Mitsubishi Electric Research Laboratories Cambridge Research Center Technical Report 2000-09 March 29, 2000 A Survey and Classification of Real Time Rendering Methods Matthias Zwicker *, Markus H. Gross

More information

Lecture 11: Ray tracing (cont.)

Lecture 11: Ray tracing (cont.) Interactive Computer Graphics Ray tracing - Summary Lecture 11: Ray tracing (cont.) Graphics Lecture 10: Slide 1 Some slides adopted from H. Pfister, Harvard Graphics Lecture 10: Slide 2 Ray tracing -

More information

Computer Graphics. Shadows

Computer Graphics. Shadows Computer Graphics Lecture 10 Shadows Taku Komura Today Shadows Overview Projective shadows Shadow texture Shadow volume Shadow map Soft shadows Why Shadows? Shadows tell us about the relative locations

More information

Image-Based Deformation of Objects in Real Scenes

Image-Based Deformation of Objects in Real Scenes Image-Based Deformation of Objects in Real Scenes Han-Vit Chung and In-Kwon Lee Dept. of Computer Science, Yonsei University sharpguy@cs.yonsei.ac.kr, iklee@yonsei.ac.kr Abstract. We present a new method

More information

Shape from Silhouettes I CV book Szelisky

Shape from Silhouettes I CV book Szelisky Shape from Silhouettes I CV book Szelisky 11.6.2 Guido Gerig CS 6320, Spring 2012 (slides modified from Marc Pollefeys UNC Chapel Hill, some of the figures and slides are adapted from M. Pollefeys, J.S.

More information

Shadow Rendering EDA101 Advanced Shading and Rendering

Shadow Rendering EDA101 Advanced Shading and Rendering Shadow Rendering EDA101 Advanced Shading and Rendering 2006 Tomas Akenine-Möller 1 Why, oh why? (1) Shadows provide cues about spatial relationships among objects 2006 Tomas Akenine-Möller 2 Why, oh why?

More information

Real-Time Shadows. Last Time? Textures can Alias. Schedule. Questions? Quiz 1: Tuesday October 26 th, in class (1 week from today!

Real-Time Shadows. Last Time? Textures can Alias. Schedule. Questions? Quiz 1: Tuesday October 26 th, in class (1 week from today! Last Time? Real-Time Shadows Perspective-Correct Interpolation Texture Coordinates Procedural Solid Textures Other Mapping Bump Displacement Environment Lighting Textures can Alias Aliasing is the under-sampling

More information

Announcements. Mosaics. How to do it? Image Mosaics

Announcements. Mosaics. How to do it? Image Mosaics Announcements Mosaics Project artifact voting Project 2 out today (help session at end of class) http://www.destination36.com/start.htm http://www.vrseattle.com/html/vrview.php?cat_id=&vrs_id=vrs38 Today

More information

Hello, Thanks for the introduction

Hello, Thanks for the introduction Hello, Thanks for the introduction 1 In this paper we suggest an efficient data-structure for precomputed shadows from point light or directional light-sources. Because, in fact, after more than four decades

More information

Image-based Modeling and Rendering: 8. Image Transformation and Panorama

Image-based Modeling and Rendering: 8. Image Transformation and Panorama Image-based Modeling and Rendering: 8. Image Transformation and Panorama I-Chen Lin, Assistant Professor Dept. of CS, National Chiao Tung Univ, Taiwan Outline Image transformation How to represent the

More information

Re-live the Movie Matrix : From Harry Nyquist to Image-Based Rendering. Tsuhan Chen Carnegie Mellon University Pittsburgh, USA

Re-live the Movie Matrix : From Harry Nyquist to Image-Based Rendering. Tsuhan Chen Carnegie Mellon University Pittsburgh, USA Re-live the Movie Matrix : From Harry Nyquist to Image-Based Rendering Tsuhan Chen tsuhan@cmu.edu Carnegie Mellon University Pittsburgh, USA Some History IEEE Multimedia Signal Processing (MMSP) Technical

More information

Real-Time Shadows. Last Time? Schedule. Questions? Today. Why are Shadows Important?

Real-Time Shadows. Last Time? Schedule. Questions? Today. Why are Shadows Important? Last Time? Real-Time Shadows The graphics pipeline Clipping & rasterization of polygons Visibility the depth buffer (z-buffer) Schedule Questions? Quiz 2: Thursday November 2 th, in class (two weeks from

More information

For Intuition about Scene Lighting. Today. Limitations of Planar Shadows. Cast Shadows on Planar Surfaces. Shadow/View Duality.

For Intuition about Scene Lighting. Today. Limitations of Planar Shadows. Cast Shadows on Planar Surfaces. Shadow/View Duality. Last Time Modeling Transformations Illumination (Shading) Real-Time Shadows Viewing Transformation (Perspective / Orthographic) Clipping Projection (to Screen Space) Graphics Pipeline Clipping Rasterization

More information

Logistics. CS 586/480 Computer Graphics II. Questions from Last Week? Slide Credits

Logistics. CS 586/480 Computer Graphics II. Questions from Last Week? Slide Credits CS 586/480 Computer Graphics II Dr. David Breen Matheson 408 Thursday 6PM Æ 8:50PM Presentation 4 10/28/04 Logistics Read research paper and prepare summary and question P. Hanrahan, "Ray Tracing Algebraic

More information

L2 Data Acquisition. Mechanical measurement (CMM) Structured light Range images Shape from shading Other methods

L2 Data Acquisition. Mechanical measurement (CMM) Structured light Range images Shape from shading Other methods L2 Data Acquisition Mechanical measurement (CMM) Structured light Range images Shape from shading Other methods 1 Coordinate Measurement Machine Touch based Slow Sparse Data Complex planning Accurate 2

More information

Stereo pairs from linear morphing

Stereo pairs from linear morphing Proc. of SPIE Vol. 3295, Stereoscopic Displays and Virtual Reality Systems V, ed. M T Bolas, S S Fisher, J O Merritt (Apr 1998) Copyright SPIE Stereo pairs from linear morphing David F. McAllister Multimedia

More information

Capturing and View-Dependent Rendering of Billboard Models

Capturing and View-Dependent Rendering of Billboard Models Capturing and View-Dependent Rendering of Billboard Models Oliver Le, Anusheel Bhushan, Pablo Diaz-Gutierrez and M. Gopi Computer Graphics Lab University of California, Irvine Abstract. In this paper,

More information

Questions from Last Week? Extra rays needed for these effects. Shadows Motivation

Questions from Last Week? Extra rays needed for these effects. Shadows Motivation CS 431/636 Advanced Rendering Techniques Dr. David Breen University Crossings 149 Tuesday 6PM 8:50PM Presentation 4 4/22/08 Questions from Last Week? Color models Light models Phong shading model Assignment

More information

CS 431/636 Advanced Rendering Techniques

CS 431/636 Advanced Rendering Techniques CS 431/636 Advanced Rendering Techniques Dr. David Breen University Crossings 149 Tuesday 6PM 8:50PM Presentation 4 4/22/08 Questions from Last Week? Color models Light models Phong shading model Assignment

More information

EECS 442 Computer vision. Announcements

EECS 442 Computer vision. Announcements EECS 442 Computer vision Announcements Midterm released after class (at 5pm) You ll have 46 hours to solve it. it s take home; you can use your notes and the books no internet must work on it individually

More information

An Efficient Visual Hull Computation Algorithm

An Efficient Visual Hull Computation Algorithm An Efficient Visual Hull Computation Algorithm Wojciech Matusik Chris Buehler Leonard McMillan Laboratory for Computer Science Massachusetts institute of Technology (wojciech, cbuehler, mcmillan)@graphics.lcs.mit.edu

More information

Lecture 8 Active stereo & Volumetric stereo

Lecture 8 Active stereo & Volumetric stereo Lecture 8 Active stereo & Volumetric stereo In this lecture, we ll first discuss another framework for describing stereo systems called active stereo, and then introduce the problem of volumetric stereo,

More information

Computer Graphics (CS 563) Lecture 4: Advanced Computer Graphics Image Based Effects: Part 1. Prof Emmanuel Agu

Computer Graphics (CS 563) Lecture 4: Advanced Computer Graphics Image Based Effects: Part 1. Prof Emmanuel Agu Computer Graphics (CS 563) Lecture 4: Advanced Computer Graphics Image Based Effects: Part 1 Prof Emmanuel Agu Computer Science Dept. Worcester Polytechnic Institute (WPI) Image Based Effects Three main

More information