Axis-aligned Filtering for Interactive Physically-based Rendering

126 Pages·2015·17.83 MB·English

Axis-aligned Filtering for Interactive Physically-based
Rendering
Soham Uday Mehta
Electrical Engineering and Computer Sciences
University of California at Berkeley
Technical Report No. UCB/EECS-2015-66
http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-66.html
May 12, 2015
Copyright © 2015, by the author(s).
All rights reserved.

Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, to republish, to post on servers or to redistribute to lists,
requires prior specific permission.
Axis-alignedFilteringforInteractivePhysically-basedRendering
by
SohamUdayMehta
Adissertationsubmittedinpartialsatisfactionofthe
requirementsforthedegreeof
DoctorofPhilosophy
in
ComputerScience
inthe
GraduateDivision
ofthe
UniversityofCalifornia,Berkeley
Committeeincharge:
ProfessorRaviRamamoorthi,Chair
ProfessorJamesO’Brien
ProfessorMartyBanks
Spring2015
Axis-alignedFilteringforInteractivePhysically-basedRendering
Copyright2015
by
SohamUdayMehta
1
Abstract
Axis-alignedFilteringforInteractivePhysically-basedRendering
by
SohamUdayMehta
DoctorofPhilosophyinComputerScience
UniversityofCalifornia,Berkeley
ProfessorRaviRamamoorthi,Chair
Computer graphics rendering is undergoing a renaissance, with physically-based rendering
methods based on accurate Monte-Carlo image synthesis replacing ad-hoc techniques in a variety
of applications including movie production. In interactive applications like product visualization
or video games, physically-based lighting effects are increasingly popular. However, producing
photo-realisticimagesatinteractivespeedsstillremainsachallenge.
InMonte-Carlorendering,apixel’scoloriscomputedbysamplingandintegratingoverahigh-
dimensionalspace. Thisincludeseffectslike(1)motionblur,duetoobjectsmovingduringthetime
the camera shutter is open; (2) defocus blur, due to camera lens optics; (3) area and environment
map lighting, which is direct illumination coming from many directions; (4) global illumination,
duetolightreflectedfromonesurfacetoanother. Thecolorissampledthroughray-orpath-tracing.
Withinsufficientrays,theimagelooksnoisybecausetheintegrandhashighvariance,and1000sof
rays are needed (per pixel) for a pleasing image. Previous work has showed a Fourier analysis for
some of these effects, deriving a compact double-wedge spectrum, and a sheared filter that aligns
with the slope of the spectrum. This filter can remove noise from a very sparsely sampled Monte-
Carlo image, but is very slow. In this thesis, we will extend the Fourier analysis for more general
cases, and propose a less compact axis-aligned filter, that aligns with the frequency axes. The
resulting spatial bandwidths are then used for image-space filtering, that is orders of magnitude
faster than sheared filtering. The packing of the Fourier spectra also provides adaptive sampling
rates that minimize noise in conjunction with the adaptive filter. These algorithms improve speed
relative to converged ground-truth by about 30(cid:0)60(cid:2), and we are able to demonstrate interactive
speed with a GPU ray-tracer. We also demonstrate an application of our method to mixed reality
withaKinectcamera.
i
Contents
Contents i
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Background 5
2.1 Radiometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 TheRenderingEquation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 FourierAnalysisandFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 SoftShadows 9
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 PreviousWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Axis-AlignedFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.5 AdaptiveSamplingRates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.6 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 DiffuseIndirectIllumination 29
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 PreviousWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.3 FourierAnalysisofIndirectIllumination . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 Axis-AlignedFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5 MultipleEffects 51
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
5.2 PreviousWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
ii
5.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.4 DefocusBlur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.5 DirectIlluminationwithdefocusblur . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.6 IndirectIllumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.7 SamplingRates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.8 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.9 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.10 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6 EnvironmentIlluminationandApplicationtoMixedReality 76
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.2 PreviousWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
6.3 DifferentialRendering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.4 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.5 Two-modeSamplingAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.6 FourierAnalysisforEnvironmentLighting . . . . . . . . . . . . . . . . . . . . . . 83
6.7 PracticalFiltering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.8 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
7 Conclusion 97
7.1 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
A BandlimitsforGlossyBRDFs 100
B MotionBlurwithSecondaryEffects 102
C Two-modeSamplingAlgorithm 105
D DerivationofChapter6,equation11 108
E VerificationofChapter6,equation17 110
Bibliography 112
iii
Acknowledgments
I want to thank my advisor Prof. Ravi Ramamoorthi for providing invaluable guidance in the
progress of my work. His deep insights in the field of rendering were indispensable for the com-
pletionofthiswork.
I thank Prof. Fredo Durand from MIT who was a co-advisor of many of my projects. In par-
ticular, his expertise in Fourier analysis was extremely helpful. I also want to thank Prof. James
O’Brien for providing advice at various points, and for having me as a GSI for the undergraduate
Computer Graphics course, which was a great learning experience for me. I would also like to
thank Prof. Carlo Sequin and Prof. Maneesh Agrawala for welcoming me into their classes and
helping my knowledge of graphics grow. I also thank Prof. Alysoha Efros and Prof. Marty Banks
for providing constructive criticism as my qualifying exam and dissertation committee members.
I want to thank my colleagues Dr. Dikpal Reddy, Dr. Jiamin Bai, Dr. Michael Tao who pro-
vided great advice on graduate school. I want to thank my collaborators and friends Ling-Qi Yan,
BrandonWangandEricYaofortheirinputsandcontributions.
This work was supported in part by NSF grant CGV 1115242, an NVIDIA PhD fellowship,
GPU donations from NVIDIA, and funding from the Intel Science and Technology Center for
VisualComputing.
1
Chapter 1
Introduction
The central focus of this thesis is speeding up photo-realistic rendering in 3-dimensional com-
puter graphics. Rendering refers to converting a 3-dimensional virtual collection of objects with
different reflective properties, viewed through a camera illuminated by some light sources, into
a 2-dimensional red-green-blue image. The scene, i.e. objects, materials, lights and camera, is
assumedtobeknown. Renderingasingleimagerequirescomputingthelightreachingthecamera
ateachpixel,andeachimagetypicallycontainsaboutamillionpixels.
1.1 Motivation
Computing the color at each pixel requires evaluation of a complicated integral involving light
energybouncingaroundinthescene,inthesteadystate. Specifically,wedealwithphoto-realistic
renderingeffects,commonlytermed“distributioneffects”,suchas
1. SoftShadows(integratingoveranarealight),
2. IndirectIllumination(integratingoverlightreflectedfromotherobjectsorsurfaces),
3. DepthofField(Integratingovertheallpointsonthecameralens),
4. MotionBlur(Integratingovertimeforwhichthecamerashutterremainsopen),
5. EnvironmentLighting(Integratingoverdistantlightingcomingfromalldirections).
The integrand consists of a high-dimensional “light field” that is a function of coordinates in
camera, light, and/or directional space. In most scenarios, the integrand light fields at adjacent or
nearby pixels overlap across multidimensional domains. But, in traditional rendering, computa-
tionalresultsarenotsharedbetweenpixels. Thesemultidimensionaleffectsareexpensive,butalso
CHAPTER1. INTRODUCTION 2
crucialforrealisminhighquality offlinerendering. Manyapproximate techniqueshavebeenpro-
posed for each effect, but they introduce bias and fail to produce the accurate result. The correct,
physically-basedsolutionisonlypossiblethroughMonte-Carlopath-tracing.
As the complexity within a pixel’s multidimensional domain increases, the computation time
requiredtorenderanimageusingMonte-Carlopath-tracingalsoincreases. Forexample,forshad-
owscastbyanarealight,asthelightsourcebecomeslargereachpixelseesmorecomplexgeometry
and the occlusion signal has more variance. Capturing this variance, i.e. reducing the noise using
previous methods requires computing more samples for each pixel, which increases render times.
Another observation is that as the complexity of these effects increases, the final image content is
often smooth. Intuitively, for shadows, the larger the size of the light source, the blurrier (softer)
the shadows. This blur in turn removes high frequencies from the final image. Putting these two
observations together we come to an ironic conclusion. Using simple Monte-Carlo ray-tracing, as
the light size increases, more rays are required, but the complexity and spatial frequencies in the
finalimageactuallydecreaseduetotheblurringorfilteringfromthelightsource–andweendup
devotingmoreandmoreresourcestocomputeasimplerandsimplerresult. Oneofthekeyinsights
is that as complexity increases inside of a single pixel, it is often true that there is a corresponding
increase in overlap between the integral domains of nearby pixels. For example, nearby pixels see
the same blockers casting shadows from the light source. Similar arguments can be applied for
other distribution effects. At an intuitive level it seems obvious that we should be able to share
information to reduce the total computation in these cases. However, robustly deriving how much
information to share and how to share it fast is a more difficult problem, and it is the problem
addressedbythisthesis.
1.2 Contributions
A large body of recent work has indicated that the number of rays in Monte-Carlo rendering can
be dramatically reduced if we share samples between pixels. Previous work has extended image
denoising techniques to Monte-Carlo images. Other work has showed a Fourier analysis for some
of these effects, deriving a compact filter that aligns with the slope of the spectrum. Although
these methods based on sheared filtering, statistical denoising or light field reconstruction can
denoise very sparsely sampled images, their practical filtering algorithms are very slow, making
them unsuitable for interactive use. In this thesis, we make a different set of tradeoffs. We use a
simple filter to reduce the number of Monte-Carlo samples considerably compared to brute force,
butlessthansomepreviousmethods. However,webenefitinhavinganextremelysimplefiltering
step,whichreducestoaspatially-varyingimage-spaceblur. Thiscanbeperformedextremelyfast,
and enables our method to be integrated in a GPU-accelerated ray-tracer. The final algorithm is
essentially an image denoiser which is very simple to implement and can operate with minimal
overhead. Our analysis also provides adaptive sampling rates which reduce noise throughout the
image. Therefore, we are able to achieve interactive frame rates while obtaining the benefits of
highqualityray-traceddistributioneffects.