Validation of Color Managed 3D Appearance Acquisition Michael Goesele Max-Planck-Institut für Informatik (MPI Informatik) Vortrag im Rahmen des V 3 D 2.

Slides:



Advertisements
Similar presentations
Introduction to Colour Management
Advertisements

Efficient Acquisition and Realistic Rendering of Car Paint Johannes Günther, Tongbo Chen, Michael Goesele, Ingo Wald, and Hans-Peter Seidel MPI Informatik.
Images Images are a key component of any multimedia presentation.
ICC profiles Understood
Multispectral Format from Perspective of Remote Sensing
Evaluating Color Descriptors for Object and Scene Recognition Koen E.A. van de Sande, Student Member, IEEE, Theo Gevers, Member, IEEE, and Cees G.M. Snoek,
Visualization and graphics research group CIPIC May 25, 2004Realistic Image Synthesis1 Tone Mapping Presented by Lok Hwa.
High Dynamic Range Imaging Samu Kemppainen VBM02S.
Measuring BRDFs. Why bother modeling BRDFs? Why not directly measure BRDFs? True knowledge of surface properties Accurate models for graphics.
Light Fields PROPERTIES AND APPLICATIONS. Outline  What are light fields  Acquisition of light fields  from a 3D scene  from a real world scene 
Foundations of Computer Graphics (Spring 2012) CS 184, Lecture 21: Radiometry Many slides courtesy Pat Hanrahan.
Advanced Computer Graphics (Spring 2013) CS 283, Lecture 8: Illumination and Reflection Many slides courtesy.
Color spaces CIE - RGB space. HSV - space. CIE - XYZ space.
Week 9 - Wednesday.  What did we talk about last time?  Fresnel reflection  Snell's Law  Microgeometry effects  Implementing BRDFs  Image based.
Fundamentals of Digital Imaging
Preserving Realism in real-time Rendering of Bidirectional Texture Functions Jan Meseth, Gero Müller, Reinhard Klein Bonn University Computer Graphics.
Illumination Model How to compute color to represent a scene As in taking a photo in real life: – Camera – Lighting – Object Geometry Material Illumination.
Deriving Lights from Pixels Presented By: WAIL ALI EL EBEEDY By: By: Web Address: ArchitectureWeek - Tools - Deriving Lights from Pixels _0528.htm.
Representations of Visual Appearance COMS 6160 [Fall 2006], Lecture 2 Ravi Ramamoorthi
Controlling Color in Displays: A discussion on Quality Jean-Baptiste Thomas Centre de Recherche et de Restauration des musées de France CNRS UMR 171 Jean-Baptiste.
1 Color Segmentation: Color Spaces and Illumination Mohan Sridharan University of Birmingham
Computer Graphics (Spring 2008) COMS 4160, Lecture 20: Illumination and Shading 2
Color Mixing There are two ways to control how much red, green, and blue light reaches the eye: “Additive Mixing” Starting with black, the right amount.
Measurement, Inverse Rendering COMS , Lecture 4.
Perceptual Evaluation of Colour Gamut Mapping Algorithms Fabienne Dugay The Norwegian Color Research Laboratory Faculty of Computer Science and Media Technology.
Computer Graphics (Fall 2008) COMS 4160, Lecture 19: Illumination and Shading 2
Color Management Systems Problems –Solve gamut matching issues –Attempt uniform appearance Solutions –Image dependent manipulations (e.g. Stone) –Device.
CSCE 641: Computer Graphics Image-based Rendering Jinxiang Chai.
Color Fidelity in Multimedia H. J. Trussell Dept. of Electrical and Computer Engineering North Carolina State University Raleigh, NC
Measure, measure, measure: BRDF, BTF, Light Fields Lecture #6
1/22/04© University of Wisconsin, CS559 Spring 2004 Last Time Course introduction Image basics.
Why Care About Color? Accurate color reproduction is commercially valuable - e.g. Kodak yellow, painting a house Color reproduction problems increased.
Exam 2: November 8 th –If you will need accommodations, please make sure you have documentation from the University Office of Disability Services –It will.
JuxtaPrism and Color Management Lorrae Famiglietti Spring 2012.
Color & Color Management. Overview I. Color Perception Definition & characteristics of color II. Color Representation RGB, CMYK, XYZ, Lab III. Color Management.
Computer Graphics Inf4/MSc Computer Graphics Lecture Notes #16 Image-Based Lighting.
Dye Sublimation Color Management
Color Model AbdelRahman Abu_absah Teacher: Dr. Sana'a Alsayegh.
Integration Of CG & Live-Action For Cinematic Visual Effects by Amarnath Director, Octopus Media School.
Perception-motivated High Dynamic Range Video Encoding
CS 376 Introduction to Computer Graphics 01 / 26 / 2007 Instructor: Michael Eckmann.
09/05/02© University of Wisconsin, CS559 Spring 2002 Last Time Course introduction Assignment 1 (not graded, but necessary) –View is part of Project 1.
Shading / Light Thanks to Srinivas Narasimhan, Langer-Zucker, Henrik Wann Jensen, Ravi Ramamoorthi, Hanrahan, Preetham.
Color Management. How does the color work?  Spectrum Spectrum is a contiguous band of wavelengths, which is emitted, reflected or transmitted by different.
Image-Based Rendering from a Single Image Kim Sang Hoon Samuel Boivin – Andre Gagalowicz INRIA.
Analysis of Subsurface Scattering under Generic Illumination Y. Mukaigawa K. Suzuki Y. Yagi Osaka University, Japan ICPR2008.
Tone Mapping Software Photomatix Pro Application to Photography Konferenz und Workshop '05 Reality-Based Visualization.
Crowdsourcing Color Perceptions using Mobile Devices Jaejeung Kim 1, Sergey Leksikov 1, Punyotai Thamjamrassri 2, Uichin Lee 1, Hyeon-Jeong Suk 2 1 Dept.
Difference Between Raster and Vector Images Raster and vector are the two basic data structures for storing and manipulating images and graphics data on.
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
03/24/03© 2003 University of Wisconsin Last Time Image Based Rendering from Sparse Data.
1 Computer Graphics Week2 –Creating a Picture. Steps for creating a picture Creating a model Perform necessary transformation Lighting and rendering the.
Rendering Synthetic Objects into Real Scenes: Bridging Traditional and Image-based Graphics with Global Illumination and High Dynamic Range Photography.
Inverse Global Illumination: Recovering Reflectance Models of Real Scenes from Photographs Computer Science Division University of California at Berkeley.
- Laboratoire d'InfoRmatique en Image et Systèmes d'information
Surround-Adaptive Local Contrast Enhancement for Preserved Detail Perception in HDR Images Geun-Young Lee 1, Sung-Hak Lee 1, Hyuk-Ju Kwon 1, Tae-Wuk Bae.
ECE 638: Principles of Digital Color Imaging Systems Lecture 3: Trichromatic theory of color.
Local Illumination and Shading
Color profiles in photography Bob Peters, GSFC Photo Club.
03/04/05© 2005 University of Wisconsin Last Time Tone Reproduction –Histogram method –LCIS and improved filter-based methods.
Greg Ward Exponent - Failure Analysis Assoc. Elena Eydelberg-Vileshin
ECE 638: Principles of Digital Color Imaging Systems Lecture 12: Characterization of Illuminants and Nonlinear Response of Human Visual System.
1 of 32 Computer Graphics Color. 2 of 32 Basics Of Color elements of color:
Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Integrated Color Solutions A presentation.
Image-Based Rendering
Chapter III, Desktop Imaging Systems and Issues: Lesson IV Working With Images
Image Based Modeling and Rendering (PI: Malik)
Consistent Presentation of Images
Introduction to Colour Management
20 November 2019 Output maps Normal Diffuse Roughness Specular
Presentation transcript:

Validation of Color Managed 3D Appearance Acquisition Michael Goesele Max-Planck-Institut für Informatik (MPI Informatik) Vortrag im Rahmen des V 3 D 2 Workshops November 2004 in Berlin

Michael Goesele Acquired BRDF Model rendered BRDF model of a carafe Villeroy & Boch (Mettlach, 19 th century)

Michael Goesele Acquired BRDF Model Which model is (more) correct?

Michael Goesele Background: BRDF bidirectional reflectance distribution function (BRDF) ratio of reflected to incident radiance at one point for any pair of directions

Michael Goesele Background: BRDF Acquisition based on acquisition system for spatially varying BRDFs [Lensch et al. 2003] determine local reflection properties for each surface point uses Lafortune BRDF model [Lafortune 1997] shown at previous V 3 D 2 Workshops …

Michael Goesele Background: BRDF Acquisition

Michael Goesele Validation Questions How exact can this method capture describe the behavior of a real object? How exact can we reproduce an objects appearance? How can this be achieved?  will be discussed in this talk How good is the Lafortune model?  will not be discussed in this talk  see e.g. [Ngan et al., SIGGRAPH Sketch 2004]

Michael Goesele Color and Computer Graphics yes, we have color … it works (somehow) RGB is always the same (?) looks nice after some tuning … more seriously … not the main concern, other technologies more important eye can adjust to bad color reproduction comparison to ground truth often not possible/required an important issue for digitization!

Michael Goesele Background: Color Management goal: ensure correct color reproduction across devices used in graphical arts and printing industry defined profile connection space (PCS) with CIE XYZ or CIE Lab color space profiles describe conversion to PCS for all devices take limitations of devices into account

Michael Goesele ICC Profile Generation example: input profile capture known test target with camera lighting conditions (spectrum) identical to finally used conditions profile generated by (commercial) software captures properties of camera lighting test target

Michael Goesele Key Idea introduce color management into BRDF acquisition and reproduction workflow convert input images into defined color space (during HDR image generation) convert rendered images into output device color space allows for objective assessment of quality of acquired models important for libraries, conservation, … color management ensures best possible color reproduction takes limitations of devices into account

Michael Goesele BRDF Acquisition Workflow move to calibrated color space move to output color space

Michael Goesele Accuracy of the Acquired Model compare spectrophotometer measurements with BRDF model evaluated under same conditions illumination at 45°, observation at 0° (along surface normal) performs measurement in spectral domain can be converted to other color representations BRDFspectrophotometer

Michael Goesele Accuracy of the Acquired Model BRDF (CIEXYZ) Spectrophotometer (CIEXYZ) EE Bird yellow59.41, 61.23, , 61.51, Bird orange42.28, 33.17, , 32.73, Bird blue34.99, 40.09, , 37.48, Bird white68.79, 73.35, , 78.75, Carafe blue17.63, 18.67, , 19.34, Carafe white57.16, 58.33, , 56.23, quality metric:  E distance in CIELab color space 1  E just noticable color difference under perfect conditions

Michael Goesele Accuracy of the Acquired Model compare renderings with photographs captured under identical conditions quality metric:  E 0  E 50  E Minerva of Arezzo (Florence, 3rd century B.C. or 1st century A.C.)

Michael Goesele Accuracy of the Acquired Model until now: only comparison of acquired model to ground truth data important for conservation, long term storage further goal: include output devices in validation can we (physically) reproduce the object correctly?

Michael Goesele Accuracy of Renderings visual comparison between rendering (on screen, printout) and real object under identical conditions all devices are calibrated no manual color adjustment was performed! screenreal object (color laser printer) printout

Michael Goesele Accuracy of Renderings visual comparison between rendering (on screen, printout) and real object under identical conditions

Michael Goesele Accuracy of Renderings visual comparison between rendering (on screen, printout) and real object under identical conditions

Michael Goesele Conclusion color management integrated into BRDF acquisition and rendering pipeline enables quantitative and visual validation of results correct color acquisition and rendering is important! approach: acquire best possible model use best possible reproduction important for long term storage reproduction technology improves (displays, printers, …) model should support these as far as possible

Michael Goesele Future Work improve quality of color management some colors are still quite problematic color management for HDR images? ongoing work in the community handling of high contrast, out-of-gamut colors tone mapping problem new display technologies (e.g., HDR display)

Michael Goesele Thanks to … Hendrik Lensch DFG Schwerpunktprogramm V 3 D 2 “Verteilte Vermittlung und Verarbeitung digitaler Dokumente” More information … Michael Goesele, Hendrik P. A. Lensch, Hans-Peter Seidel: Validation of Color Managed 3D Appearance Acquisition. Proc. IS&T’s 12th Color Imaging Conference, pp , Hendrik P. A. Lensch, Jan Kautz, Michael Goesele, Wolfgang Heidrich, Hans-Peter Seidel: Image-Based Reconstruction of Spatial Appearance and Geometric Detail. ACM Transactions on Graphics, vol. 22, 2, pp , 2003.