Light and shading. Source: A. Efros

Similar documents
COMP 558 lecture 6 Sept. 27, 2010

Lighting and Shading. Outline. Raytracing Example. Global Illumination. Local Illumination. Radiosity Example

Capturing light. Source: A. Efros

Capturing light. Source: A. Efros

Normals. In OpenGL the normal vector is part of the state Set by glnormal*()

EE 584 MACHINE VISION

The Nature of Light. Chapter 22. Geometric Optics Using a Ray Approximation. Ray Approximation

Computer Graphics. Shading. Page. Copyright Gotsman, Elber, Barequet, Karni, Sheffer Computer Science, Technion. The Physics

27 Refraction, Dispersion, Internal Reflection

Computer Graphics. Surface Rendering Methods. Content. Polygonal rendering. Global rendering. November 14, 2005

Physics 11b Lecture #19

Chapter 18: Ray Optics Questions & Problems

Illumination Distribution from Shadows

Lecture 7 7 Refraction and Snell s Law Reading Assignment: Read Kipnis Chapter 4 Refraction of Light, Section III, IV

Apparent Depth. B' l'

Intro to Scientific Computing: Solutions

Lenses and Imaging (Part I)

Pattern Recognition Systems Lab 1 Least Mean Squares

Lenses and imaging. MIT 2.71/ /10/01 wk2-a-1

. Perform a geometric (ray-optics) construction (i.e., draw in the rays on the diagram) to show where the final image is formed.

A Practical Method for Estimation of Point Light-Sources

EECS 442 Computer vision. Multiple view geometry Affine structure from Motion

Propagation of light: rays versus wave fronts; geometrical and physical optics

EECS 442 Computer vision. Multiple view geometry Affine structure from Motion

Final Exam information

Lecture # 09: Flow visualization techniques: schlieren and shadowgraphy

Two View Geometry Part 2 Fundamental Matrix Computation

The isoperimetric problem on the hypercube

A Selected Primer on Computer Vision: Geometric and Photometric Stereo & Structured Light

FINITE DIFFERENCE TIME DOMAIN METHOD (FDTD)

AP B mirrors and lenses websheet 23.2

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Accuracy Improvement in Camera Calibration

Eigenimages. Digital Image Processing: Bernd Girod, 2013 Stanford University -- Eigenimages 1

Lenses and Imaging (Part I) Parabloid mirror: perfect focusing

Vision & Perception. Simple model: simple reflectance/illumination model. image: x(n 1,n 2 )=i(n 1,n 2 )r(n 1,n 2 ) 0 < r(n 1,n 2 ) < 1

CS6670: Computer Vision

Basic Optics: Index of Refraction

Practical Implementation at tri-ace

Single-view Metrology and Camera Calibration

Why Do We Care About Lighting? Computer Graphics Lighting. The Surface Normal. Flat Shading (Per-face) Setting a Surface Normal in OpenGL

OpenGL Illumination example. 2IV60 Computer graphics set 8: Illumination Models and Surface-Rendering Methods. Introduction 2.

Single-view Metrology and Camera Calibration

Administrative UNSUPERVISED LEARNING. Unsupervised learning. Supervised learning 11/25/13. Final project. No office hours today

Panel for Adobe Premiere Pro CC Partner Solution

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Derivation of perspective stereo projection matrices with depth, shape and magnification consideration

Aberrations in Lens & Mirrors (Hecht 6.3)

Image Segmentation EEE 508

Parabolic Path to a Best Best-Fit Line:

Structure from motion

EVALUATION OF TRIGONOMETRIC FUNCTIONS

MATHEMATICAL METHODS OF ANALYSIS AND EXPERIMENTAL DATA PROCESSING (Or Methods of Curve Fitting)

Using the Keyboard. Using the Wireless Keyboard. > Using the Keyboard

World Scientific Research Journal (WSRJ) ISSN: Research on Fresnel Lens Optical Receiving Antenna in Indoor Visible

WebAssign Lesson 6-1b Geometric Series (Homework)

Assigning colour to pixels or fragments. Modelling Illumination. We shall see how it is done in a rasterization model. CS475/CS675 - Lecture 14

Numerical Methods Lecture 6 - Curve Fitting Techniques

Math 10C Long Range Plans

EM375 STATISTICS AND MEASUREMENT UNCERTAINTY LEAST SQUARES LINEAR REGRESSION ANALYSIS

Module 8-7: Pascal s Triangle and the Binomial Theorem

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem

A Resource for Free-standing Mathematics Qualifications

Lecture 18. Optimization in n dimensions

Wavelet Transform. CSE 490 G Introduction to Data Compression Winter Wavelet Transformed Barbara (Enhanced) Wavelet Transformed Barbara (Actual)

Lecture 13: Validation

Dynamic Programming and Curve Fitting Based Road Boundary Detection

Ones Assignment Method for Solving Traveling Salesman Problem

Xbar/R Chart for x1-x3

SD vs. SD + One of the most important uses of sample statistics is to estimate the corresponding population parameters.

Assignment 5; Due Friday, February 10

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

Fundamentals of Media Processing. Shin'ichi Satoh Kazuya Kodama Hiroshi Mo Duy-Dinh Le

condition w i B i S maximum u i

Rendering. Ray Tracing

9 x and g(x) = 4. x. Find (x) 3.6. I. Combining Functions. A. From Equations. Example: Let f(x) = and its domain. Example: Let f(x) = and g(x) = x x 4

Adaptive Processing of SAR Data for ATR

Our Learning Problem, Again

Section 4. Imaging and Paraxial Optics

Section 4. Imaging and Paraxial Optics

Light. Properties of light. What is light? Today What is light? How do we measure it? How does light propagate? How does light interact with matter?

PLEASURE TEST SERIES (XI) - 04 By O.P. Gupta (For stuffs on Math, click at theopgupta.com)

Image based Cats and Possums Identification for Intelligent Trapping Systems

Python Programming: An Introduction to Computer Science

OCR Statistics 1. Working with data. Section 3: Measures of spread

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19

Radiometry and reflectance

Normal Distributions

Visualization of Gauss-Bonnet Theorem

The Virtual Point Light Source Model the Practical Realisation of Photometric Stereo for Dynamic Surface Inspection

Lights, Surfaces, and Cameras. Light sources emit photons Surfaces reflect & absorb photons Cameras measure photons

South Slave Divisional Education Council. Math 10C

How to Select the Best Refractive Index

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Designing a learning system

Big-O Analysis. Asymptotics

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Name Date Hr. ALGEBRA 1-2 SPRING FINAL MULTIPLE CHOICE REVIEW #2

Revealing Historical Background of Bayon Faces Using Classification

Normal Map Acquisition of Nearly Flat Objects Using a Flatbed. Scanner

Transcription:

Light ad shadig Source: A. Efros

Image formatio What determies the brightess of a image piel? Sesor characteristics Light source properties Eposure Surface shape ad orietatio Optics Surface reflectace properties Slide b L. Fei-Fei

Fudametal radiometric relatio L: Radiace emitted from P toward P Eerg carried b a ra Watts per sq. meter per steradia E: Irradiace fallig o P from the les Eerg arrivig at a surface Watts per sq. meter P d α P f z What is the relatioship betwee E ad L? Szeliski..3

Fudametal radiometric relatio P d α P E d = π cos 4 α 4 f L f z Image irradiace is liearl related to scee radiace Irradiace is proportioal to the area of the les ad iversel proportioal to the squared distace betwee the les ad the image plae The irradiace falls off as the agle betwee the viewig ra ad the optical ais icreases Szeliski..3

Fudametal radiometric relatio E π 4 = d cos 4 f α L S. B. Kag ad R. Weiss Ca we calibrate a camera usig a image of a flat tetureless Lambertia surface? ECCV 000.

From light ras to piel values X = E Δt E d = π cos 4 α 4 f L Z = f E Δt Camera respose fuctio: the mappig f from irradiace to piel values Useful if we wat to estimate material properties Eables us to create high damic rage images For more ifo: P. E. Debevec ad J. Malik Recoverig High Damic Rage Radiace Maps from Photographs SIGGRAPH 97

The iteractio of light ad surfaces What happes whe a light ra hits a poit o a object? Some of the light gets absorbed coverted to other forms of eerg e.g. heat Some gets trasmitted through the object possibl bet through refractio or scattered iside the object subsurface scatterig Some gets reflected possibl i multiple directios at oce Reall complicated thigs ca happe fluorescece Bidirectioal reflectace distributio fuctio BRDF How bright a surface appears whe viewed from oe directio whe light falls o it from aother Defiitio: ratio of the radiace i the emitted directio to irradiace i the icidet directio Source: Steve Seitz

BRDFs ca be icredibl complicated

Diffuse reflectio Light is reflected equall i all directios Dull matte surfaces like chalk or late pait Microfacets scatter icomig light radoml Effect is that light is reflected equall i all directios Brightess of the surface depeds o the icidece of illumiatio brighter darker

Diffuse reflectio: Lambert s law θ S B = = ρ ρ S S cos θ B: radiosit total power leavig the surface per uit area ρ: albedo fractio of icidet irradiace reflected b the surface : uit ormal S: source vector magitude proportioal to itesit of the source

Specular reflectio Radiatio arrivig alog a source directio leaves alog the specular directio source directio reflected about ormal Some fractio is absorbed some reflected O real surfaces eerg usuall goes ito a lobe of directios Phog model: reflected eerg falls of with cos δθ Lambertia + specular model: sum of diffuse ad specular term

Specular reflectio Movig the light source Chagig the epoet

Role of specularit i computer visio

Photometric stereo shape from shadig Ca we recostruct the shape of a object based o shadig cues? Luca della Robbia Catoria 438

Photometric stereo Assume: A Lambertia object A local shadig model each poit o a surface receives light ol from sources visible at that poit A set of kow light source directios A set of pictures of a object obtaied i eactl the same camera/object cofiguratio but usig differet sources Orthographic projectio Goal: recostruct object shape ad albedo S S S??? F&P d ed. sec...4

Eample Recovered albedo Recovered ormal field Recovered surface model F&P d ed. sec...4

Eample Iput Recovered albedo Recovered ormal field Recovered surface model z

Image model Kow: source vectors S j ad piel values I j Ukow: surface ormal ad albedo ρ F&P d ed. sec...4

j j j j k k I V g S S = = = ρ ρ Image model Kow: source vectors S j ad piel values I j Ukow: surface ormal ad albedo ρ Assume that the respose fuctio of the camera is a liear scalig b a factor of k Lambert s law: F&P d ed. sec...4

Least squares problem For each piel set up a liear sstem:! # # # # "# I I! I kow $! & # & # & = # & # %& # " V T V T Obtai least-squares solutio for g which we defied as ρ Sice is the uit ormal ρ is give b the magitude of g Fiall = g / ρ! V T $ & & & g & & % 3 3 kow ukow F&P d ed. sec...4

Sthetic eample Recovered albedo Recovered ormal field F&P d ed. sec...4

Recall the surface is writte as This meas the ormal has the form: Recoverig a surface from ormals If we write the estimated vector g as The we obtai values for the partial derivatives of the surface: f + + = f f f f = 3 g g g g / / 3 3 g g f g g f = = F&P d ed. sec...4

Recoverig a surface from ormals We ca ow recover the surface height at a poit b itegratio alog some path e.g. Itegrabilit: for the surface f to eist the mied secod partial derivatives must be equal: f = f s 0ds + 0 0 f tdt + C g g / g / g 3 3 = for robustess should take itegrals over ma differet paths ad average the results i practice the should at least be similar F&P d ed. sec...4

Surface recovered b itegratio F&P d ed. sec...4

Limitatios Orthographic camera model Simplistic reflectace ad lightig model o shadows o iterreflectios o missig data Itegratio is trick

Assigmet Iput Recovered albedo Recovered ormal field Recovered surface model z

Fidig the directio of the light source = z z z z I I I S S S!!!! = I I I S S!!! I = S Full 3D case: For poits o the occludig cotour: P. illius ad J.-O. Ekludh Automatic estimatio of the projected light source directio CVPR 00 S

Fidig the directio of the light source P. illius ad J.-O. Ekludh Automatic estimatio of the projected light source directio CVPR 00

Applicatio: Detectig composite photos Fake photo Real photo M. K. Johso ad H. Farid Eposig Digital Forgeries b Detectig Icosistecies i Lightig ACM Multimedia ad Securit Workshop 005.