Shape from Shading and Texture

Slides:



Advertisements
Similar presentations
Computer Vision Radiometry. Bahadir K. Gunturk2 Radiometry Radiometry is the part of image formation concerned with the relation among the amounts of.
Advertisements

Announcements Project 2 due today Project 3 out today –demo session at the end of class.
Computer Vision Spring ,-685 Instructor: S. Narasimhan Wean 5403 T-R 3:00pm – 4:20pm Lecture #12.
Lecture 35: Photometric stereo CS4670 / 5670 : Computer Vision Noah Snavely.
3D Modeling from a photograph
Capturing light Source: A. Efros. Image formation How bright is the image of a scene point?
Computer Vision CS 776 Spring 2014 Photometric Stereo and Illumination Cones Prof. Alex Berg (Slide credits to many folks on individual slides)
Basic Principles of Surface Reflectance
Torrance Sparrow Model of Reflectance + Oren Nayar Model of Reflectance.
1. What is Lighting? 2 Example 1. Find the cubic polynomial or that passes through the four points and satisfies 1.As a photon Metal Insulator.
Shape-from-X Class 11 Some slides from Shree Nayar. others.
December 5, 2013Computer Vision Lecture 20: Hidden Markov Models/Depth 1 Stereo Vision Due to the limited resolution of images, increasing the baseline.
Lecture 23: Photometric Stereo CS4670/5760: Computer Vision Kavita Bala Scott Wehrwein.
Course Review CS/ECE 181b Spring Topics since Midterm Stereo vision Shape from shading Optical flow Face recognition project.
Capturing light Source: A. Efros. Review Pinhole projection models What are vanishing points and vanishing lines? What is orthographic projection? How.
May 2004SFS1 Shape from shading Surface brightness and Surface Orientation --> Reflectance map READING: Nalwa Chapter 5. BKP Horn, Chapter 10.
3-D Computer Vision CSc83029 / Ioannis Stamos 3-D Computer Vision CSc Radiometry and Reflectance.
Visibility Subspaces: Uncalibrated Photometric Stereo with Shadows Kalyan Sunkavalli, Harvard University Joint work with Todd Zickler and Hanspeter Pfister.
Basic Principles of Surface Reflectance
Computer Vision Sources, shading and photometric stereo Marc Pollefeys COMP 256.
Lecture 32: Photometric stereo, Part 2 CS4670: Computer Vision Noah Snavely.
CS292 Computational Vision and Language Visual Features - Colour and Texture.
Basic Principles of Surface Reflectance Lecture #3 Thanks to Shree Nayar, Ravi Ramamoorthi, Pat Hanrahan.
Information that lets you recognise a region.
Photometric Stereo Merle Norman Cosmetics, Los Angeles Readings R. Woodham, Photometric Method for Determining Surface Orientation from Multiple Images.
Photometric Stereo & Shape from Shading
Computer Graphics Inf4/MSc Computer Graphics Lecture Notes #16 Image-Based Lighting.
Computer Vision Spring ,-685 Instructor: S. Narasimhan PH A18B T-R 10:30am – 11:50am Lecture #13.
Lecture 12 Stereo Reconstruction II Lecture 12 Stereo Reconstruction II Mata kuliah: T Computer Vision Tahun: 2010.
Computer Vision - A Modern Approach Set: Sources, shadows and shading Slides by D.A. Forsyth Sources and shading How bright (or what colour) are objects?
Analysis of Lighting Effects Outline: The problem Lighting models Shape from shading Photometric stereo Harmonic analysis of lighting.
Y. Moses 11 Combining Photometric and Geometric Constraints Yael Moses IDC, Herzliya Joint work with Ilan Shimshoni and Michael Lindenbaum, the Technion.
Shape from Shading and Texture. Lambertian Reflectance Model Diffuse surfaces appear equally bright from all directionsDiffuse surfaces appear equally.
CS447/ Realistic Rendering -- Radiosity Methods-- Introduction to 2D and 3D Computer Graphics.
December 4, 2014Computer Vision Lecture 22: Depth 1 Stereo Vision Comparing the similar triangles PMC l and p l LC l, we get: Similarly, for PNC r and.
Capturing light Source: A. Efros.
Sources, Shadows, and Shading
Course 9 Texture. Definition: Texture is repeating patterns of local variations in image intensity, which is too fine to be distinguished. Texture evokes.
Radiometry and Photometric Stereo 1. Estimate the 3D shape from shading information Can you tell the shape of an object from these photos ? 2.
Understanding the effect of lighting in images Ronen Basri.
Lec 22: Stereo CS4670 / 5670: Computer Vision Kavita Bala.
Course 10 Shading. 1. Basic Concepts: Light Source: Radiance: the light energy radiated from a unit area of light source (or surface) in a unit solid.
Interreflections : The Inverse Problem Lecture #12 Thanks to Shree Nayar, Seitz et al, Levoy et al, David Kriegman.
Diffuse Reflections from Rough Surfaces Lecture #5
Announcements Office hours today 2:30-3:30 Graded midterms will be returned at the end of the class.
November 4, THE REFLECTANCE MAP AND SHAPE-FROM-SHADING.
1 Chapter 5: Sources, Shadows, Shading Light source: Anything emits light that is internally generated Exitance: The internally generated power per unit.
Shape from Shading Course web page: vision.cis.udel.edu/cv February 26, 2003  Lecture 6.
Multiple Light Source Optical Flow Multiple Light Source Optical Flow Robert J. Woodham ICCV’90.
1Ellen L. Walker 3D Vision Why? The world is 3D Not all useful information is readily available in 2D Why so hard? “Inverse problem”: one image = many.
Active Lighting for Appearance Decomposition Todd Zickler DEAS, Harvard University.
Tal Amir Advanced Topics in Computer Vision May 29 th, 2015 COUPLED MOTION- LIGHTING ANALYSIS.
Understanding the effect of lighting in images Ronen Basri.
EECS 274 Computer Vision Sources, Shadows, and Shading.
Announcements Project 3a due today Project 3b due next Friday.
1 Resolving the Generalized Bas-Relief Ambiguity by Entropy Minimization Neil G. Alldrin Satya P. Mallick David J. Kriegman University of California, San.
Advanced Computer Graphics
MAN-522 Computer Vision Spring
CS4670 / 5670: Computer Vision Kavita Bala Lec 27: Stereo.
Merle Norman Cosmetics, Los Angeles
Common Classification Tasks
Capturing light Source: A. Efros.
Announcements Project 3b due Friday.
Depthmap Reconstruction Based on Monocular cues
Announcements Today: evals Monday: project presentations (8 min talks)
Lecture 28: Photometric Stereo
Announcements Project 3 out today demo session at the end of class.
Chapter 11: Stereopsis Stereopsis: Fusing the pictures taken by two cameras and exploiting the difference (or disparity) between them to obtain the depth.
CS 480/680 Computer Graphics Shading.
Physical Problem of Simultaneous Linear Equations in Image Processing
Presentation transcript:

Shape from Shading and Texture

Lambertian Reflectance Model Diffuse surfaces appear equally bright from all directions For point illumination, brightness proportional to cos q

Lambertian Reflectance Model Therefore, for a constant-colored object with distant illumination, can write E = L r ln E = observed brightness L = brightness of light source r = reflectance (albedo) of surface l = direction to light source n = surface normal

Shape from Shading The above equation contains some information about shape, and in some cases is enough to recover shape completely (in theory) if L, r, and l are known Similar to integration (surface normal is like a derivative), but only know a part of derivative Have to assume surface continuity

Shape from Shading Assume surface is given by Z(x,y) Let In this case, surface normal is

Shape from Shading So, write Discretize: end up with one equation per pixel But this is p equations in 2p unknowns…

Shape from Shading Integrability constraint: Wind up with system of 2p (nonlinear) differential equations No solution in presence of noise

Estimating Illumination and Albedo Need to know surface reflectance and Illumination brightness and direction In general, can’t compute from single image Certain assumptions permit estimating these Assume uniform distribution of normals, look at distribution of intensities in image Insert known reference object into image Slightly specular object: estimate lighting from specular highlights, then discard pixels in highlights

Variational Shape from Shading Approach: energy minimization Given observed E(x,y), find shape Z(x,y) that minimizes energy Regularization: minimize combination of disparity w. data, surface curvature

Variational Shape from Shading Solve by techniques from calculus of variations Use Euler-Lagrange equations to get a PDE, solve numerically Unlike with snakes, “greedy” methods tend not to work well

Enforcing Integrability Let fZ be the Fourier transform of Z, fp and fq be Fourier transforms of p and q Then For nonintegrable p and q these aren’t equal

Enforcing Integrability Construct and recompute The new p’ and q’ are the integrable equations closest to the original p and q

Difficulties with Shape from Shading Robust estimation of L, r, l? Shadows Non-Lambertian surfaces More than 1 light, or “diffuse illumination” Interreflections

Shape from Shading Results [Trucco & Verri]

Shape from Shading Results

Active Shape from Shading Idea: several (user-controlled) light sources More data Allows determining surface normal directly Allows spatially-varying reflectance Redundant measurements: discard shadows and specular highlights Often called “photometric stereo”

Photometric Stereo Setup [Rushmeier et al., 1997]

Photometric Stereo Math For each point p, can write Constant a incorporates light source brightness, camera sensitivity, etc.

Photometric Stereo Math Solving above equation gives (r /a) n n must be unit-length  uniquely determined Determine r up to global constant With more than 3 light sources: Discard highest and lowest measurements If still more, solve by least squares

Photometric Stereo Results Recovered normals (re-lit) Input images Recovered color [Rushmeier et al., 1997]

Helmholtz Stereopsis Based on Helmholtz reciprocity: any BRDF is the same under interchange of light, viewer So, take pairs of observations w. viewer, light interchanged Ratio of the observations in a pair is independent of BRDF

[Zickler, Belhumeur, & Kriegman] Helmholtz Stereopsis [Zickler, Belhumeur, & Kriegman]

Helmholtz Stereopsis

Texture Texture: repeated pattern on a surface Elements (“textons”) either identical or come from some statistical distribution Shape from texture comes from looking at deformation of individual textons or from distribution of textons on a surface

Shape from Texture Much the same as shape from shading, but have more information Foreshortening: gives surface normal (not just one component, as in shape from shading) Perspective distortion: gives information about depth directly Sparse depth information (only at textons) About the same as shape from shading, because of smoothness term in energy eqn.

Shape from Texture Results [Forsyth]