1. Computer Vision CS 776 Spring 2014 Cameras & Photogrammetry 3 Prof. Alex Berg (Slide credits to many folks on individual slides)

2. Cameras & Photogrammetry 3 http://www.math.tu- dresden.de/DMV2000/Impress/PI C003.jpg Gamma Scientific http://www.autobild.de/artikel/xeno nlampen-im-test-2822873.html Adapted from: http://en.wikipedia.org/wiki/File:Commer cial_Integrating_Sphere.jpg

3. Stories of light Alex Berg 2012Jensen & Buhler Siggraph 2002

4. Image formation What determines the brightness of an image pixel? Light source properties Surface shape and orientation Surface reflectance properties Optics Sensor characteristics Slide by L. Fei-Fei Exposure

5. Fundamental radiometric relation L: Radiance emitted from P toward P’ E: Irradiance falling on P’ from the lens What is the relationship between E and L? Szeliski 2.2.3 P P’ f z d α

6. Fundamental radiometric relation Image irradiance is linearly related to scene radiance Irradiance is proportional to the area of the lens and inversely proportional to the squared distance between the lens and the image plane The irradiance falls off as the angle between the viewing ray and the optical axis increases Szeliski 2.2.3 P P’ f z d α

7. The interaction of light and surfaces What happens when a light ray hits a point on an object? Some of the light gets absorbed –converted to other forms of energy (e.g., heat) Some gets transmitted through the object –possibly bent, through “refraction” –Or scattered inside the object (subsurface scattering) Some gets reflected –possibly in multiple directions at once Really complicated things can happen –fluorescence Bidirectional reflectance distribution function (BRDF): how bright a surface appears when viewed from one direction when light falls on it from another Slide by Steve Seitz

8. BRDFs can be incredibly complicated… Slide by Svetlana Lazebnik

9. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Radiometry Questions: –how “ bright ” will surfaces be? –what is “ brightness ” ? measuring light interactions between light and surfaces Core idea - think about light arriving at a surface around any point is a hemisphere of directions Simplest problems can be dealt with by reasoning about this hemisphere

10. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Lambert ’ s wall

11. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth More complex wall

12. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Foreshortening Principle: two sources that look the same to a receiver must have the same effect on the receiver. Principle: two receivers that look the same to a source must receive the same amount of energy. “ look the same ” means produce the same input hemisphere (or output hemisphere) Reason: what else can a receiver know about a source but what appears on its input hemisphere? (ditto, swapping receiver and source) Crucial consequence: a big source (resp. receiver), viewed at a glancing angle, must produce (resp. experience) the same effect as a small source (resp. receiver) viewed frontally.

13. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Solid Angle By analogy with angle (in radians), the solid angle subtended by a region at a point is the area projected on a unit sphere centered at that point The solid angle subtended by a patch area dA is given by Another useful expression:

14. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Measuring Light in Free Space Desirable property: in a vacuum, the relevant unit does not go down along a straight line. How do we get a unit with this property? Think about the power transferred from an infinitesimal source to an infinitesimal receiver. We have total power leaving s to r = total power arriving at r from s Also: Power arriving at r is proportional to: –solid angle subtended by s at r (because if s looked bigger from r, there ’ d be more) –foreshortened area of r (because a bigger r will collect more power

15. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Radiance All this suggests that the light transferred from source to receiver should be measured as: Radiant power per unit foreshortened area per unit solid angle This is radiance Units: watts per square meter per steradian (wm -2 sr -1 ) Usually written as: Crucial property: In a vacuum, radiance leaving p in the direction of q is the same as radiance arriving at q from p –which was how we got to the unit

16. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Radiance is constant along straight lines Power 1->2, leaving 1: Power 1->2, arriving at 2: But these must be the same, so that the two radiances are equal

17. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Irradiance How much light is arriving at a surface? Sensible unit is Irradiance Incident power per unit area not foreshortened This is a function of incoming angle. A surface experiencing radiance L(x  ) coming in from d  experiences irradiance Crucial property: Total power arriving at the surface is given by adding irradiance over all incoming angles --- this is why it ’ s a natural unit Total power is

18. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Light at surfaces Many effects when light strikes a surface -- could be: –absorbed –transmitted skin –reflected mirror –scattered milk –travel along the surface and leave at some other point sweaty skin Assume that –surfaces don ’ t fluoresce e.g. scorpions, washing powder –surfaces don ’ t emit light (i.e. are cool) –all the light leaving a point is due to that arriving at that point

19. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth The BRDF Assuming that –surfaces don ’ t fluoresce –surfaces don ’ t emit light (i.e. are cool) –all the light leaving a point is due to that arriving at that point Can model this situation with the Bidirectional Reflectance Distribution Function (BRDF) the ratio of the radiance in the outgoing direction to the incident irradiance

20. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth BRDF Units: inverse steradians (sr -1 ) Symmetric in incoming and outgoing directions - this is the Helmholtz reciprocity principle Radiance leaving a surface in a particular direction: –add contributions from every incoming direction

21. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Suppressing Angles - Radiosity In many situations, we do not really need angle coordinates –e.g. cotton cloth, where the reflected light is not dependent on angle Appropriate radiometric unit is radiosity –total power leaving a point on the surface, per unit area on the surface (Wm -2 ) –note that this is independent of the direction Radiosity from radiance? –sum radiance leaving surface over all exit directions, multiplying by a cosine because this is per unit area not per unit foreshortened area

22. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Radiosity Important relationship: –radiosity of a surface whose radiance is independent of angle (e.g. that cotton cloth)

23. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Suppressing the angles in the BRDF BRDF is a very general notion –some surfaces need it (underside of a CD; tiger eye; etc) –very hard to measure,illuminate from one direction, view from another, repeat –very unstable minor surface damage can change the BRDF e.g. ridges of oil left by contact with the skin can act as lenses for many surfaces, light leaving the surface is largely independent of exit angle – surface roughness is one source of this property

24. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Directional hemispheric reflectance Directional hemispheric reflectance: –the fraction of the incident irradiance in a given direction that is reflected by the surface (whatever the direction of reflection) –unitless, range is 0-1 Note that DHR varies with incoming direction –eg a ridged surface, where left facing ridges are absorbent and right facing ridges reflect.

25. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Lambertian surfaces and albedo For some surfaces, the DHR is independent of illumination direction too –cotton cloth, carpets, matte paper, matte paints, etc. For such surfaces, radiance leaving the surface is independent of angle Called Lambertian surfaces (same Lambert) or ideal diffuse surfaces Use radiosity as a unit to describe light leaving the surface DHR is often called diffuse reflectance, or albedo for a Lambertian surface, BRDF is independent of angle, too. Useful fact:

26. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Specular surfaces Another important class of surfaces is specular, or mirror- like. –radiation arriving along a direction leaves along the specular direction –reflect about normal –some fraction is absorbed, some reflected –on real surfaces, energy usually goes into a lobe of directions –can write a BRDF, but requires the use of funny functions

27. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Phong ’ s model There are very few cases where the exact shape of the specular lobe matters. Typically: –very, very small --- mirror –small -- blurry mirror –bigger -- see only light sources as “ specularities ” –very big -- faint specularities Phong ’ s model –reflected energy falls off with

28. Computer Vision - A Modern Approach Set: Radiometry Slides by D.A. Forsyth Lambertian + specular Widespread model –all surfaces are Lambertian plus specular component Advantages –easy to manipulate –very often quite close true Disadvantages –some surfaces are not e.g. underside of CD ’ s, feathers of many birds, blue spots on many marine crustaceans and fish, most rough surfaces, oil films (skin!), wet surfaces –Generally, very little advantage in modelling behaviour of light at a surface in more detail -- it is quite difficult to understand behaviour of L+S surfaces

29. Diffuse reflection Light is reflected equally in all directions Dull, matte surfaces like chalk or latex paint Microfacets scatter incoming light randomly Effect is that light is reflected equally in all directions Brightness of the surface depends on the incidence of illumination brighter darker Slide by Svetlana Lazebnik

30. Diffuse reflection: Lambert’s law N S B: radiosity (total power leaving the surface per unit area) ρ: albedo (fraction of incident irradiance reflected by the surface) N: unit normal S: source vector (magnitude proportional to intensity of the source) θ Slide by Svetlana Lazebnik

31. Specular reflection Radiation arriving along a source direction leaves along the specular direction (source direction reflected about normal) Some fraction is absorbed, some reflected On real surfaces, energy usually goes into a lobe of directions Phong model: reflected energy falls of with Lambertian + specular model: sum of diffuse and specular term Slide by Svetlana Lazebnik

32. Specular reflection Moving the light source Changing the exponent Slide by Svetlana Lazebnik

33. Fundamental radiometric relation Application: S. B. Kang and R. Weiss, Can we calibrate a camera using an image of a flat, textureless Lambertian surface? ECCV 2000.Can we calibrate a camera using an image of a flat, textureless Lambertian surface? Slide by Svetlana Lazebnik

34. Photometric stereo (shape from shading) Can we reconstruct the shape of an object based on shading cues? Luca della Robbia, Cantoria, 1438 Slide by Svetlana Lazebnik

35. Photometric stereo Assume: A Lambertian object A local shading model (each point on a surface receives light only from sources visible at that point) A set of known light source directions A set of pictures of an object, obtained in exactly the same camera/object configuration but using different sources Orthographic projection Goal: reconstruct object shape and albedo SnSn ??? S1S1 S2S2 F&P 2 nd ed., sec. 2.2.4

36. Surface model: Monge patch F&P 2 nd ed., sec. 2.2.4

37. Image model Known: source vectors S j and pixel values I j (x,y) Unknown: normal N(x,y) and albedo ρ(x,y) Assume that the response function of the camera is a linear scaling by a factor of k Lambert’s law: F&P 2 nd ed., sec. 2.2.4

38. Least squares problem Obtain least-squares solution for g(x,y) (which we defined as N(x,y)  (x,y) ) Since N(x,y) is the unit normal,  (x,y) is given by the magnitude of g(x,y) Finally, N(x,y) = g(x,y) /  (x,y) (n × 1) known unknown (n × 3)(3 × 1) For each pixel, set up a linear system: F&P 2 nd ed., sec. 2.2.4

39. Example Recovered albedo Recovered normal field F&P 2 nd ed., sec. 2.2.4

40. Recall the surface is written as This means the normal has the form: Recovering a surface from normals If we write the estimated vector g as Then we obtain values for the partial derivatives of the surface: F&P 2 nd ed., sec. 2.2.4

41. Recovering a surface from normals Integrability: for the surface f to exist, the mixed second partial derivatives must be equal: We can now recover the surface height at any point by integration along some path, e.g. (for robustness, can take integrals over many different paths and average the results) (in practice, they should at least be similar) F&P 2 nd ed., sec. 2.2.4

42. Surface recovered by integration F&P 2 nd ed., sec. 2.2.4

43. Finding the direction of the light source I(x,y) = N(x,y) ·S(x,y) + A Full 3D case: For points on the occluding contour: P. Nillius and J.-O. Eklundh, “Automatic estimation of the projected light source direction,” CVPR 2001 N S Slide by Svetlana Lazebnik

44. Finding the direction of the light source P. Nillius and J.-O. Eklundh, “Automatic estimation of the projected light source direction,” CVPR 2001 Slide by Svetlana Lazebnik

45. Application: Detecting composite photos Fake photo Real photo M. K. Johnson and H. Farid, Exposing Digital Forgeries by Detecting Inconsistencies in Lighting, ACM Multimedia and Security Workshop, 2005.Exposing Digital Forgeries by Detecting Inconsistencies in Lighting Slide by Svetlana Lazebnik

46. From light rays to pixel values Camera response function: the mapping f from irradiance to pixel values Useful if we want to estimate material properties Enables us to create high dynamic range images For more info: P. E. Debevec and J. Malik, Recovering High Dynamic Range Radiance Maps from Photographs, SIGGRAPH 97Recovering High Dynamic Range Radiance Maps from Photographs Slide by Svetlana Lazebnik

47. Limitations Orthographic camera model Simplistic reflectance and lighting model No shadows No interreflections No missing data Integration is tricky Slide by Svetlana Lazebnik

48. More reading & thought problems (reconstructing from a single image using priors) Shape, Illumination, and Reflectance from Shading Jonathan T. Barron, Jitendra Malik Tech Report, 2013 (implementing this one is the extra credit) Recovering High Dynamic Range Radiance Maps from Photographs. Paul E. Debevec and Jitendra Malik. SIGGRAPH 1997 (people can perceive reflectance) Surface reflectance estimation and natural illumination statistics. R.O. Dror, E.H. Adelson, and A.S. Willsky. Workshop on Statistical and Computational Theories of Vision 2001