1. 03/2/05© 2005 University of Wisconsin Last Time Sub-surface and Atmospheric Scattering

2. 03/2/05© 2005 University of Wisconsin Today Tone-Reproduction: Part 1 The NPR presentation assignment Projects

3. 03/2/05© 2005 University of Wisconsin Dynamic Range Real scenes contain both very bright and very dark regions Dynamic range is the ratio of the brightest to darkest points in the scene Standard measurement is candelas per m 2 (defined shortly) For example, in the interior of the church the dynamic range is 100,000 to 1

4. 03/2/05© 2005 University of Wisconsin Tone Reproduction The human eye can globally adapt to about 10 9 :1 –Adjusts for the average brightness we perceive in one scene –Global adaptation lets us see in very low light or very bright conditions –But it’s slow - how slow? The human eye can locally adapt to about 10,000:1 –In a single scene (global adaptation level), we can perceive contrast across this range Most display devices have a very limited dynamic range –On the order of 100:1 for a very good monitor or film Tone reproduction is the problem of making the 10,000:1 scene look right on a 100:1 display device

5. 03/2/05© 2005 University of Wisconsin Different Goals Option 1: Reveal as much detail as possible in the image given the limited dynamic range –For conveying information, but not necessarily realism Option 2: Reveal what would be available if the viewer would really be there –Perceptual limits place limits on what we can see under a given set of viewing conditions

6. 03/2/05© 2005 University of Wisconsin An Artist’s Approach Artists have known how to do this for centuries e.g. Vermeer (spot the tricks)

7. 03/2/05© 2005 University of Wisconsin More Vermeer

8. 03/2/05© 2005 University of Wisconsin Measurements We have discussed things in terms of radiometry, with quantities such as Watts, meters, seconds, steradians Photometry deals with radiometric quantities pushed through a response function –A sensor (the human eye) has a response curve, V –Luminance (candelas per meter squared, cd/m 2 ) is the integral of radiance against the luminous efficiency function (the sensor response function)

9. 03/2/05© 2005 University of Wisconsin Luminous Efficiency

10. 03/2/05© 2005 University of Wisconsin Color and Luminance The CIE XYZ color space is intended to encode luminance in the Y channel To get from RGB to XYZ, apply the following linear transform: –Taken from Sillion and Puech, other sources differ

11. 03/2/05© 2005 University of Wisconsin CIE (Y,x,y) The XYZ space includes brightness info with color info –X and Z get bigger as the color gets brighter To avoid this, use (Y,x,y) –x=X/(X+Y+Z) –y=Y/(X+Y+Z) (x,y) are chromaticity values

12. 03/2/05© 2005 University of Wisconsin Automatic Tone Reproduction There are three main classes of solutions: –Global operators find a mapping from image to display luminance that is the same for every pixel –Local operators change the mapping from one pixel to the next –Perceptually guided operators use elements of human perception to guide the tone reproduction process

13. 03/2/05© 2005 University of Wisconsin Linear Mappings The simplest thing to do is to linearly map the highest intensity in the image to the highest display intensity, or the lowest to the lowest –This gives very bad results, shown for mapping the maps lowest to lowest

14. 03/2/05© 2005 University of Wisconsin Non-Linear Mappings Instead of linear, define some other mapping: L d =M(L w ) –Display luminance is some function of the world luminance It is important to retain relative brightness, but not absolute brightness –If one point is brighter than another in the source, it should be brighter in the output –The mapping M should be strictly increasing

15. 03/2/05© 2005 University of Wisconsin Histogram Methods (Ward Larson, Rushmeier and Piatko, TVCG 97) In any one scene, the dynamic range is not filled uniformly The aim of histogram methods is to generate a mapping that “fills in the gaps” in the range

16. 03/2/05© 2005 University of Wisconsin Building Histograms Work in brightness: B=log 10 (L) –Humans are more sensitive to “brightness,” but the formula is a hack The histogram is a count of how many pixels have each brightness –Choose a set of bins –Break the image into chunks that subtend about 1  of arc Assumes you know the camera –Average brightness in each chunk –Count chunks that fall in each bin, f(b i ) –Result is graph on previous image

17. 03/2/05© 2005 University of Wisconsin Cumulative Distribution We can consider the histogram counts as the probability of seeing a pixel in each range The cumulative distribution function is defined:

18. 03/2/05© 2005 University of Wisconsin Histogram Equalization The aim is an output histogram in which all the bins are roughly equally filled The naïve way to do this is to set: Then, go through and convert brightness back into luminance for display

19. 03/2/05© 2005 University of Wisconsin Naïve Histogram Linear left. Histogram right. What went wrong?

20. 03/2/05© 2005 University of Wisconsin Avoiding Super-Sensitivity We should make sure that we do not increase contrast beyond the linear mapping contrast: This imposes a constraint on the frequency in each bin: Reduce the count of bins that exceed the maximum –Have to iterate, because changing counts changes T

21. 03/2/05© 2005 University of Wisconsin Better Histogram Adjustment Old New

22. 03/2/05© 2005 University of Wisconsin Cumulative Distributions When does it fail to converge? When does it fail to maintain contrast?

23. 03/2/05© 2005 University of Wisconsin More Histogram Methods The process can be further adapted to handle human contrast sensitivity –Avoid wasting range on things that cannot be resolved –Explicitly make sure that irresolvable features remain that way Details next lecture

24. 03/2/05© 2005 University of Wisconsin Local Methods Histogram methods do poorly if there is too much dynamic range in the input –Can’t avoid reducing contrast below what should be resolvable Local methods exploit the local nature of contrast –You don’t compare the brightness of two things across the room, only neighboring points These methods are justified (in part) by the following: –Pixel intensity depends on the product of reflectance and illumination –Reflectance changes fast – keep this –Illumination changes slowly – change this

25. 03/2/05© 2005 University of Wisconsin Basic Idea Filter to separate high and low frequencies –Filter to get a high-pass and low pass version of the image –Recall, a smoothing filter keeps only low frequencies Compress the low frequencies –Reduce the contrast in the low pass portion –Using diffusion filters, typically Add back in the high frequencies –Add the reduced low-pass to the high-pass to get a new image LCIS is one algorithm –Tumblin and Turk, SIGGRAPH 99

26. 03/2/05© 2005 University of Wisconsin What’s the Problem?

27. 03/2/05© 2005 University of Wisconsin For Comparison

28. 03/2/05© 2005 University of Wisconsin LCIS Problems Luminance does change quickly sometimes –At shadow boundaries and occlusion boundaries The filtering has to be carefully adapted to make sure the reflectance and lighting components are separated –Standard problem is a dark halo around a bright object –Sharp changes at shadows get interpreted as shading –When added back into contrast reduced base layer, they give artifacts –This can be fixed

29. 03/2/05© 2005 University of Wisconsin Local Tone Reproduction The filter you use should be edge preserving –A filter that smoothes out regions with low gradient, but does not smooth across high gradients

30. 03/2/05© 2005 University of Wisconsin Edge Preserving Filters (Durand and Dorsey, SIGGRAPH 2002) The idea is to take a smoothing filter, say a Gaussian, and multiply it by a function that reduces the weight at sharp edges (and influence function) –It turns out, this is closely related to robust statistical estimation – the problem of ignoring outliers in statistical data

31. 03/2/05© 2005 University of Wisconsin Robust Estimation Instead of least squares estimation, use something that reduces the weight of outliers from the mean –Least squares minimizes sum of squared differences

32. 03/2/05© 2005 University of Wisconsin Results Right, a full image Below, still some artifacts at edges –Fix by detecting unreliable pixels and smoothing

33. 03/2/05© 2005 University of Wisconsin The NPR Assignment A.Choose a paper related to non-photorealistic rendering B.Read it thoroughly and work back through any important references C.Prepare a 20 minute talk on the contents of the paper D.Give your presentation in class March 28, 30, April 1, 4, 6

34. 03/2/05© 2005 University of Wisconsin A. Choosing a Paper Your paper must have been published in an academic conference Good conferences include: –SIGGRAPH: most NPR papers are in the final session, 1995- –NPAR (Non-photorealistic animation and rendering): three so far, 2000, 2002 and 2004 –I3D (Symposium on Interactive 3D Graphics): a few NPAR papers Most papers are online now, as is some video –I have a good selection of proceedings and videos First person to send me the title gets the paper (no duplicates) –Choose a paper by March 11 (Friday + week)

35. 03/2/05© 2005 University of Wisconsin B. Read the Paper Reading an academic paper may be new to you Look for the main idea: –What are they trying to do? –What techniques are they using? How can it be summarized? –What work does it rely upon? Track down any very important references –Things that are essential to know in order to understand the paper

36. 03/2/05© 2005 University of Wisconsin C. Prepare a Talk Issues to address include: –What is the goal of the work? –What came before? –What is the technical contribution? –Examples –Future work? Aim for 20 minutes –PRACTICE AND TIME YOURSELF

37. 03/2/05© 2005 University of Wisconsin D. Give your Talk Powerpoint is the easiest option –If you want to use something else, let me know Heuristically, one slide lasts for two minutes Video is possible, but tell me ahead of time Leave time for questions –Your prepared talk should time in at 17 minutes or so Scheduling will be under my control

38. 03/2/05© 2005 University of Wisconsin Grading Approximately 20% of your grade Based on: –How well you convey the essence of the paper –How well you appear to understand the contents –Your ability to give a talk, including timing and organization Talk to me about your presentation before you give it This is intended to be a learning experience

39. 03/2/05© 2005 University of Wisconsin Class Projects Most of the grade for this class will be based upon a class project It must be heavily related to rendering (NOT modeling) You can work in pairs, or alone –Pairs should do twice as much An “A” project would implement something interesting (need not be novel) Novel things can get published, and I’m more interested in those Ideas? Some have been mentioned in class, but you can do whatever I approve of Due by Friday of exam week (May 13)

40. 03/2/05© 2005 University of Wisconsin Suitable Projects A project suggestion page will be up today, and may be added to over the coming days Anything labeled a level 3, or a combination of level 2, exercises from PBRT are good candidates All projects must be approved by March 18