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

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

Real Time Rendering. CS 563 Advanced Topics in Computer Graphics. Songxiang Gu Jan, 31, 2005

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

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

Image or Object? Is this real?

Image-Based Modeling and Rendering

Image Base Rendering: An Introduction

The Light Field and Image-Based Rendering

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

Hybrid Rendering for Collaborative, Immersive Virtual Environments

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

Image-Based Modeling and Rendering

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

A Warping-based Refinement of Lumigraphs

A Survey and Classification of Real Time Rendering Methods

Frédo Durand, George Drettakis, Joëlle Thollot and Claude Puech

Per-Object Image Warping with Layered Impostors

Image-Based Rendering and Light Fields

Image-Based Rendering using Image-Warping Motivation and Background

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

A Hybrid LOD-Sprite Technique for Interactive Rendering of Large Datasets

A Panoramic Walkthrough System with Occlusion Culling

CSc Topics in Computer Graphics 3D Photography

Fundamentals of image formation and re-use

Image-Based Rendering

Projective Texture Mapping with Full Panorama

History of computer graphics

Enabling immersive gaming experiences Intro to Ray Tracing

Multi-layered impostors for accelerated rendering

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

Spatially-Encoded Far-Field Representations for Interactive Walkthroughs

Image Based Rendering

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

CS 431/636 Advanced Rendering Techniques

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

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

FAST ALGORITHM FOR CREATING IMAGE-BASED STEREO IMAGES

Light Fields. Johns Hopkins Department of Computer Science Course : Rendering Techniques, Professor: Jonathan Cohen

Point-Based Impostors for Real-Time Visualization

Capturing and View-Dependent Rendering of Billboard Models

Tour Into the Picture using a Vanishing Line and its Extension to Panoramic Images

View Dependent Layered Projective Texture Maps

Evolution of Imaging Technology in Computer Graphics. Related Areas

Modeling Light. Slides from Alexei A. Efros and others

Level of Details in Computer Rendering

Efficient Impostor Manipulation for Real-Time Visualization of Urban Scenery

Georgios Tziritas Computer Science Department

GeForce4. John Montrym Henry Moreton

CSE528 Computer Graphics: Theory, Algorithms, and Applications

Lazy decompression of surface light fields for precomputed global illumination

More and More on Light Fields. Last Lecture

Rendering. Converting a 3D scene to a 2D image. Camera. Light. Rendering. View Plane

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

Enhancing Traditional Rasterization Graphics with Ray Tracing. October 2015

Image-Based Rendering. Image-Based Rendering

Point based Rendering

Modeling Light. Michal Havlik

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

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

Temporally Coherent Interactive Ray Tracing

Layered depth images. The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters.

IMAGE-BASED RENDERING

3/1/2010. Acceleration Techniques V1.2. Goals. Overview. Based on slides from Celine Loscos (v1.0)

Choosing the right course

03 RENDERING PART TWO

Temporally Coherent Interactive Ray Tracing

Point Sample Rendering

A Frequency Analysis of Light Transport

Computer Graphics 1. Chapter 7 (June 17th, 2010, 2-4pm): Shading and rendering. LMU München Medieninformatik Andreas Butz Computergraphik 1 SS2010

Modeling Light. Michal Havlik

View-based Rendering: Visualizing Real Objects from Scanned Range and Color Data

Image-based rendering using plane-sweeping modelisation

Course Title: Computer Graphics Course no: CSC209

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

Lecturer Athanasios Nikolaidis

Real-Time Rendering (Echtzeitgraphik) Michael Wimmer

Towards a Perceptual Method of Blending for Image-Based Models

CHAPTER 1 Graphics Systems and Models 3

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

A Reconfigurable Architecture for Load-Balanced Rendering

CS230 : Computer Graphics Lecture 4. Tamar Shinar Computer Science & Engineering UC Riverside

VIDEO FOR VIRTUAL REALITY LIGHT FIELD BASICS JAMES TOMPKIN

IMAGE-BASED MODELING & RENDERING (1)

CS 563 Advanced Topics in Computer Graphics QSplat. by Matt Maziarz

Unstructured Lumigraph Rendering

Computational Photography

Deep Compression for Streaming Texture Intensive Animations

Screen-Space Triangulation for Interactive Point Rendering

A Complete and Practical System for Interactive. Walkthroughs of Arbitrarily Complex Scenes

Stateless Remote Environment Navigation with View Compression

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

Modeling Light. Michal Havlik : Computational Photography Alexei Efros, CMU, Fall 2011

Forward Image Mapping

L1 - Introduction. Contents. Introduction of CAD/CAM system Components of CAD/CAM systems Basic concepts of graphics programming

Real-Time Reflection on Moving Vehicles in Urban Environments

Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping

Interactive Computer Graphics A TOP-DOWN APPROACH WITH SHADER-BASED OPENGL

To Do. Advanced Computer Graphics. The Story So Far. Course Outline. Rendering (Creating, shading images from geometry, lighting, materials)

Practical Techniques for Ray Tracing in Games. Gareth Morgan (Imagination Technologies) Aras Pranckevičius (Unity Technologies) March, 2014

Introduction. Chapter Computer Graphics

Transcription:

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 power and architectures New applications, demands and markets Giant databases (digital mock up) virtual reality, games... Image-based graphics: current state and trends potentialities

A million polygons

Who needs a million polygons? Assemblies of CAD models Integrated design/manufacturing Digital mock-ups

A million pixels

Rendering in Computer Graphics Models for 3D geometry, light reflection Global illumination simulation Real-time rendering All of these requirements present difficult challenges!

Subtle illumination effects

Real-time rendering for dynamic scenes

Image-based rendering (IBR) Avoid expensive/difficult 3D model Start from a set of images Manipulate pixels to create new image With real images, elaborate lighting effects are free QuicktimeVR [Chen95], [Laveau], [McMillan95,97],...

What's an image? array of RGB (α)( ) samples add depth sample add multiple depths, normals... (Layered Depth Image, LDI)

Tour into the picture [Horry 97] Use a single image Manually define simple perspective Manually create layers with selected portions of the image See http://www-syntim.inria.fr/~horry/images/s97slide.html

Layered depth images [Gortler97] See http://www.research.microsoft.com/research/graphics/cohen/sig_97_ibr/index.htm Gather multiple depth samples for each pixel

Layered depth images Reproject all samples in new image no need for depth comparisons splatting technique

Rendering from a million polygons? Transform 1-3M vertices Lighting Texturing Memory bandwidth Raster engine, z-buffering 20 M flop 10 M flop 15 M flop 100 Mb 100 Mb?

Rendering from a million pixels? Transform 1M points (coherence) No lighting No z-buffering Memory bandwith (coherent access) 6 M flop 8 Mb

Rendering performance considerations 3D rendering reaches the consumer market thousands of lit,textured polygons / second. specialized boards require careful design for efficient integration. Image processing subsystems video (analog/digital), texture (games), multimedia extensions

Generating and obtaining IBR models From synthetic images Ray tracing Range images, LDIs, Lumigraphs From real images use panoramic views, vision techniques feature matching (difficult) Lumigraphs (no depth)

Link with vision Image based modeling (IBM...) Use images + parameters avoid WYSIAYG object class information interactive modeling (facade)

IBR = sampling + reconstruction Operate without geometry More complete representations (higher dimensionality) Simplified representations (adding simplified 3D model)

Light field - Lumigraph Levoy, Hanrahan 96 Gortler et al 96 Slide used with permission (M. Levoy, Pat Hanrahan) See http://www-graphics.stanford.edu/projects/lightfield

Light field - Lumigraph sampling Slide used with permission (M. Levoy, Pat Hanrahan)

Impostors Create textured 3D model from images simplified representation rendered as 3D geometry Planar polygons [Maciel95, Schaufler96, Shade96] 3D meshes from range images [Pulli 97, Darsa 97, Sillion 97]

Textured 3D mesh from a range image Pulli 97 Slide used with permission (K. Pulli et al.)

Blending required to combine views without blending (z-buffer) with blending [Pulli 97] Images used with permission (K. Pulli et al.) See http://www.cs.washington.edu/homes/kapu

Principles of our approach: example

Local model (3D objects)

Distant model (3D objects)

Impostor (Textured 3D mesh)

Combined model (local+impostor)

Combined model (local+impostor)

Deforming impostors Talisman [Torborg 96] Talisman Render sprites Layered model Affine transforms [Lengyel97] Impostor transition Slide used with permission (J. Lengyel et al, Microsoft research.) See http://research.microsoft.com/~jedl

Applications for IBR Walkthrough / view synthesis Stereo synthesis Interpolation/extrapolation Latency compensation Frame rate equalization Network transmission Leverage expensive rendering

Polygons Pixels Continuous Modeling Animation Level of detail Discrete Capture Video streams Filtering

Pixels Discrete, regular nature easy to filter: adaptation to user perceptual limitations Work with real images Easy to capture Let nature do the modeling/lighting Work from existing images (historical, legal, forensic applications...) WYSIAYG

Polygons Complete 3D model solid modeling global illumination path planning, assembly checking, collision detection Common denominator for many modeling systems Can be simplified but it's hard to keep the model consistent

Extended notion of image-based models Use both images and 3D data Combine a simplified model with images model can be extracted from images or other information

IBR and availability of 3D models Complete 3D model IBR as graphics subsystem No 3D model QTVR, plenoptic rendering The model is the image(s) Range data available Scanned data is huge: need to simplify

Problems with current algorithms Holes in reconstructed images Image deformation (impostors) Volume of data Sampling/filtering artifacts

Can we expect hardware advances? view interpolator soft z-buffering and blending multiple or view-dependent textures decompression memory bandwith

Limitations of IBR Specularities Lighting/geometry/reflectance changes are hard Computer Vision issues: model building Images may not be available!

Marketability QTVR, panoramic images Image-based modeling Image-based rendering architectures Image caching, impostors Network applications (QoS) Light field

...and now? Simulation of global illumination Visibility calculations View-dependent texture mapping disparity/depth specularity/shading re-lighting Compression of depth values

Computer-augmented reality input panorama Drettakis 97 computed solution

Conclusions IBR offers useful advances leverage cost of high-quality rendering fast extension via specialized subsystem Vision issues limit applicability of pure IBR for real images Use combined 3D models and images Polygons are still useful!

Acknowledgements Yann Argotti, George Drettakis, Frédo Durand, Peter Kipfer, Céline Loscos, Stéphane Moreau, Cyril Soler Michael F. Cohen, Steven Gortler Marc Levoy, Pat Hanrahan, Kari Pulli, Jed Lengyel, Youichi Horry, Ken-ichi Anjyo, Kiyoshi Arai