A Theoretical Framework for Physically Based Rendering
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Photorealistic Texturing for Dummies
PHOTOREALISTIC TEXTURING FOR DUMMIES Leigh Van Der Byl http://leigh.cgcommunity.com Compiled by Carlos Eduardo de Paula - Brazil PHOTOREALISTIC TEXTURING FOR DUMMIES Index Part 1 – An Introduction To Texturing ...............................................................................................3 Introduction ........................................................................................................................................................ 3 Why Procedural Textures just don't work ...................................................................................................... 3 Observing the Aspects of Surfaces In Real Life............................................................................................ 4 The Different Aspects Of Real World Surfaces ............................................................................................ 5 Colour................................................................................................................................................................... 5 Diffuse.................................................................................................................................................................. 5 Luminosity........................................................................................................................................................... 6 Specularity........................................................................................................................................................... -
Path Tracing
Path Tracing Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017 Irradiance Circumference of a Circle • The circumference of a circle of radius r is 2πr • In fact, a radian is defined as the angle you get on an arc of length r • We can represent the circumference of a circle as an integral 2휋 푐푖푟푐 = 푟푑휑 0 • This is essentially computing the length of the circumference as an infinite sum of infinitesimal segments of length 푟푑휑, over the range from 휑=0 to 2π Area of a Hemisphere • We can compute the area of a hemisphere by integrating ‘rings’ ranging over a second angle 휃, which ranges from 0 (at the north pole) to π/2 (at the equator) • The area of a ring is the circumference of the ring times the width 푟푑휃 • The circumference is going to be scaled by sin휃 as the rings are smaller towards the top of the hemisphere 휋 2 2휋 2 2 푎푟푒푎 = sin 휃 푟 푑휑 푑휃 = 2휋푟 0 0 Hemisphere Integral 휋 2 2휋 2 푎푟푒푎 = sin 휃 푟 푑휑 푑휃 0 0 • We are effectively computing an infinite summation of tiny rectangular area elements over a two dimensional domain. Each element has dimensions: sin 휃 푟푑휑 × 푟푑휃 Irradiance • Now, let’s assume that we are interested in computing the total amount of light arriving at some point on a surface • We are essentially integrating over all possible directions in the hemisphere above the surface (assuming it’s opaque and we’re not receiving translucent light from below the surface) • We are integrating the light intensity (or radiance) coming in from every direction • The total incoming radiance over all directions in the hemisphere -
Ray Tracing Notes CS445 Computer Graphics, Fall 2012 (Last Modified
Ray Tracing Notes CS445 Computer Graphics, Fall 2012 (last modified 10/7/12) Jenny Orr, Willamette University 1 The Ray Trace Algorithm refracted ray re!ected ray camera pixel Light3 light ray computed ray screen in shadow Light1 Light2 Figure 1 1 For each pixel in image { 2 compute ray 3 pixel_color = Trace(ray) 4 } 5 color Trace(ray) { 6 For each object 7 find intersection (if any) 8 check if intersection is closest 9 If no intersection exists 10 color = background color 11 else for the closest intersection 12 for each light // local color 13 color += ambient 14 if (! inShadow(shadowRay)) color += diffuse + specular 15 if reflective 16 color += k_r Trace(reflected ray) 17 if refractive 18 color += k_t Trace(transmitted ray) 19 return color 20 } 21 boolean inShadow(shadowRay) { 22 for each object 23 if object intersects shadowRay return true 24 return false 25 } 1 1.1 Complexity w = width of image h = height of image n = number of objects l = number of lights d = levels of recursion for reflection/refraction Assuming no recursion or shadows O(w ∗ h ∗ (n + l ∗ n)). How does this change if shadows and recursion are added? What about anti-aliasing? 2 Computing the Ray In general, points P on a ray can be expressed parametrically as P = P0 + t dir where P0 is the starting point, dir is a unit vector pointing in the ray's direction, and t ≥ 0 is the parameter. When t = 0, P corresponds to P0 and as t increases, P moves along the ray. In line 2 of the code, we calculate a ray which starts at the camera P0 and points from P0 to the given pixel located at P1 on a virtual screen (view plane), as shown in Figure 2. -
Adjoints and Importance in Rendering: an Overview
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 9, NO. 3, JULY-SEPTEMBER 2003 1 Adjoints and Importance in Rendering: An Overview Per H. Christensen Abstract—This survey gives an overview of the use of importance, an adjoint of light, in speeding up rendering. The importance of a light distribution indicates its contribution to the region of most interest—typically the directly visible parts of a scene. Importance can therefore be used to concentrate global illumination and ray tracing calculations where they matter most for image accuracy, while reducing computations in areas of the scene that do not significantly influence the image. In this paper, we attempt to clarify the various uses of adjoints and importance in rendering by unifying them into a single framework. While doing so, we also generalize some theoretical results—known from discrete representations—to a continuous domain. Index Terms—Rendering, adjoints, importance, light, ray tracing, global illumination, participating media, literature survey. æ 1INTRODUCTION HE use of importance functions started in neutron importance since it indicates how much the different parts Ttransport simulations soon after World War II. Im- of the domain contribute to the solution at the most portance was used (in different disguises) from 1983 to important part. Importance is also known as visual accelerate ray tracing [2], [16], [27], [78], [79]. Smits et al. [67] importance, view importance, potential, visual potential, value, formally introduced the use of importance for global -
Shadow and Specularity Priors for Intrinsic Light Field Decomposition
Shadow and Specularity Priors for Intrinsic Light Field Decomposition Anna Alperovich, Ole Johannsen, Michael Strecke, and Bastian Goldluecke University of Konstanz [email protected] Abstract. In this work, we focus on the problem of intrinsic scene decompo- sition in light fields. Our main contribution is a novel prior to cope with cast shadows and inter-reflections. In contrast to other approaches which model inter- reflection based only on geometry, we model indirect shading by combining ge- ometric and color information. We compute a shadow confidence measure for the light field and use it in the regularization constraints. Another contribution is an improved specularity estimation by using color information from sub-aperture views. The new priors are embedded in a recent framework to decompose the input light field into albedo, shading, and specularity. We arrive at a variational model where we regularize albedo and the two shading components on epipolar plane images, encouraging them to be consistent across all sub-aperture views. Our method is evaluated on ground truth synthetic datasets and real world light fields. We outperform both state-of-the art approaches for RGB+D images and recent methods proposed for light fields. 1 Introduction Intrinsic image decomposition is one of the fundamental problems in computer vision, and has been studied extensively [23,3]. For Lambertian objects, where an input image is decomposed into albedo and shading components, numerous solutions have been pre- sented in the literature. Depending on the input data, the approaches can be divided into those dealing with a single image [36,18,14], multiple images [39,24], and image + depth methods [9,20]. -
LIGHT TRANSPORT SIMULATION in REFLECTIVE DISPLAYS By
LIGHT TRANSPORT SIMULATION IN REFLECTIVE DISPLAYS by Zhanpeng Feng, B.S.E.E., M.S.E.E. A Dissertation In ELECTRICAL ENGINEERING DOCTOR OF PHILOSOPHY Dr. Brian Nutter Chair of the Committee Dr. Sunanda Mitra Dr. Tanja Karp Dr. Richard Gale Dr. Peter Westfall Peggy Gordon Miller Dean of the Graduate School May, 2012 Copyright 2012, Zhanpeng Feng Texas Tech University, Zhanpeng Feng, May 2012 ACKNOWLEDGEMENTS The pursuit of my Ph.D. has been a rather long journey. The journey started when I came to Texas Tech ten years ago, then took a change in direction when I went to California to work for Qualcomm in 2006. Over the course, I am privileged to have met the most amazing professionals in Texas Tech and Qualcomm. Without them I would have never been able to finish this dissertation. I begin by thanking my advisor, Dr. Brian Nutter, for introducing me to the brilliant world of research, and teaching me hands on skills to actually get something done. Dr. Nutter sets an example of excellence that inspired me to work as an engineer and researcher for my career. I would also extend my thanks to Dr. Mitra for her breadth and depth of knowledge; to Dr. Karp for her scientific rigor; to Dr. Gale for his expertise in color science and sense of humor; and to Dr. Westfall, for his great mind in statistics. They have provided me invaluable advice throughout the research. My colleagues and supervisors in Qualcomm also gave tremendous support and guidance along the path. Tom Fiske helped me establish my knowledge in optics from ground up and taught me how to use all the tools for optical measurements. -
The Photon Mapping Method
7 The Photon Mapping Method “I get by with a little help from my friends.” —John Lennon, 1940–1980 HOTON mapping is a practical approach for computing global illumination within complex P environments. Much like irradiance caching methods, photon mapping caches and reuses illumination information in the scene for efficiency. Photon mapping has also been successfully applied for computing lighting within, and in the presence of, participating media. In this chapter we briefly introduce the photon mapping technique. This sets the foundation for our contributions in the next chapter, which make volumetric photon mapping practical. 7.1 Algorithm Overview Photon mapping, introduced by Jensen[1995; 1996; 1997; 1998; 2001], is a practical approach for computing global illumination. At a high level, the algorithm consists of two main steps: Algorithm 7.1:PHOTONMAPPING() 1 PHOTONTRACING(); 2 RENDERUSINGPHOTONMAP(); In the first step, a lighting simulation is performed by tracing packets of energy, or photons, from light sources and storing these photons as they scatter within the scene. This processes 119 120 Algorithm 7.2:PHOTONTRACING() 1 n 0; e Æ 2 repeat 3 (l, pdf (l)) = CHOOSELIGHT(); 4 (xp , ~!p , ©p ) = GENERATEPHOTON(l ); ©p 5 TRACEPHOTON(xp , ~!p , pdf (l) ); 6 n 1; e ÅÆ 7 until photon map full ; 1 8 Scale power of all photons by ; ne results in a set of photon maps, which can be used to efficiently query lighting information. In the second pass, the final image is rendered using Monte Carlo ray tracing. This rendering step is made more efficient by exploiting the lighting information cached in the photon map. -
3D Computer Graphics Compiled By: H
animation Charge-coupled device Charts on SO(3) chemistry chirality chromatic aberration chrominance Cinema 4D cinematography CinePaint Circle circumference ClanLib Class of the Titans clean room design Clifford algebra Clip Mapping Clipping (computer graphics) Clipping_(computer_graphics) Cocoa (API) CODE V collinear collision detection color color buffer comic book Comm. ACM Command & Conquer: Tiberian series Commutative operation Compact disc Comparison of Direct3D and OpenGL compiler Compiz complement (set theory) complex analysis complex number complex polygon Component Object Model composite pattern compositing Compression artifacts computationReverse computational Catmull-Clark fluid dynamics computational geometry subdivision Computational_geometry computed surface axial tomography Cel-shaded Computed tomography computer animation Computer Aided Design computerCg andprogramming video games Computer animation computer cluster computer display computer file computer game computer games computer generated image computer graphics Computer hardware Computer History Museum Computer keyboard Computer mouse computer program Computer programming computer science computer software computer storage Computer-aided design Computer-aided design#Capabilities computer-aided manufacturing computer-generated imagery concave cone (solid)language Cone tracing Conjugacy_class#Conjugacy_as_group_action Clipmap COLLADA consortium constraints Comparison Constructive solid geometry of continuous Direct3D function contrast ratioand conversion OpenGL between -
Lighting - the Radiance Equation
CHAPTER 3 Lighting - the Radiance Equation Lighting The Fundamental Problem for Computer Graphics So far we have a scene composed of geometric objects. In computing terms this would be a data structure representing a collection of objects. Each object, for example, might be itself a collection of polygons. Each polygon is sequence of points on a plane. The ‘real world’, however, is ‘one that generates energy’. Our scene so far is truly a phantom one, since it simply is a description of a set of forms with no substance. Energy must be generated: the scene must be lit; albeit, lit with virtual light. Computer graphics is concerned with the construction of virtual models of scenes. This is a relatively straightforward problem to solve. In comparison, the problem of lighting scenes is the major and central conceptual and practical problem of com- puter graphics. The problem is one of simulating lighting in scenes, in such a way that the computation does not take forever. Also the resulting 2D projected images should look as if they are real. In fact, let’s make the problem even more interesting and challenging: we do not just want the computation to be fast, we want it in real time. Image frames, in other words, virtual photographs taken within the scene must be produced fast enough to keep up with the changing gaze (head and eye moves) of Lighting - the Radiance Equation 35 Great Events of the Twentieth Century35 Lighting - the Radiance Equation people looking and moving around the scene - so that they experience the same vis- ual sensations as if they were moving through a corresponding real scene. -
CS 184: Problems and Questions on Rendering
CS 184: Problems and Questions on Rendering Ravi Ramamoorthi Problems 1. Define the terms Radiance and Irradiance, and give the units for each. Write down the formula (inte- gral) for irradiance at a point in terms of the illumination L(ω) incident from all directions ω. Write down the local reflectance equation, i.e. express the net reflected radiance in a given direction as an integral over the incident illumination. 2. Make appropriate approximations to derive the radiosity equation from the full rendering equation. 3. Match the surface material to the formula (and goniometric diagram shown in class). Also, give an example of a real material that reasonably closely approximates the mathematical description. Not all materials need have a corresponding diagram. The materials are ideal mirror, dark glossy, ideal diffuse, retroreflective. The formulae for the BRDF fr are 4 ka(R~ · V~ ), kb(R~ · V~ ) , kc/(N~ · V~ ), kdδ(R~), ke. 4. Consider the Cornell Box (as in the radiosity lecture, assume for now that this is essentially a room with only the walls, ceiling and floor. Assume for now, there are no small boxes or other furniture in the room, and that all surfaces are Lambertian. The box also has a small rectangular white light source at the center of the ceiling.) Assume we make careful measurements of the light source intensity and dimensions of the room, as well as the material properties of the walls, floor and ceiling. We then use these as inputs to our simple OpenGL renderer. Assuming we have been completely accurate, will the computer-generated picture be identical to a photograph of the same scene from the same location? If so, why? If not, what will be the differences? Ignore gamma correction and other nonlinear transfer issues. -
LEAN Mapping
LEAN Mapping Marc Olano∗ Dan Bakery Firaxis Games Firaxis Games (a) (b) (c) Figure 1: In-game views of a two-layer LEAN map ocean with sun just off screen to the right, and artist-selected shininess equivalent to a Blinn-Phong specular exponent of 13,777: (a) near, (b) mid, and (c) far. Note the lack of aliasing, even with an extremely high power. Abstract 1 Introduction For over thirty years, bump mapping has been an effective method We introduce Linear Efficient Antialiased Normal (LEAN) Map- for adding apparent detail to a surface [Blinn 1978]. We use the ping, a method for real-time filtering of specular highlights in bump term bump mapping to refer to both the original height texture that and normal maps. The method evaluates bumps as part of a shading defines surface normal perturbation for shading, and the more com- computation in the tangent space of the polygonal surface rather mon and general normal mapping, where the texture holds the ac- than in the tangent space of the individual bumps. By operat- tual surface normal. These methods are extremely common in video ing in a common tangent space, we are able to store information games, where the additional surface detail allows a rich visual ex- on the distribution of bump normals in a linearly-filterable form perience without complex high-polygon models. compatible with standard MIP and anisotropic filtering hardware. The necessary textures can be computed in a preprocess or gener- Unfortunately, bump mapping has serious drawbacks with filtering ated in real-time on the GPU for time-varying normal maps. -
Light (Technical)
Chapter 21 Light (technical) In this chapter we will describe in more detail how light and reflections are properly measured and represented. These concepts may not be necessary for doing casual com- puter graphics, but they can become important in order to do high quality rendering. Such high quality rendering is often done using stand alone software and does not use the same rendering pipeline as OpenGL. We will cover some of this material, as it is perhaps the most developed part of advanced computer graphics. This chapter will be covering material at a more advanced level than the rest of this book. For an even more detailed treatment of this material, see Jim Arvo’s PhD thesis [3] and Eric Veach’s PhD thesis [71]. There are two steps needed to understand high quality light simulation. First of all, one needs to understand the proper units needed to measure light and reflection. This understanding directly leads to equations which model how light behaves in a scene. Secondly, one needs algorithms that compute approximate solutions to these equations. These algorithms make heavy use of the ray tracing infrastructure described in Chapter 20. In this chapter we will focuson the more fundamental aspect of deriving the appropriate equations, and only touch on the subsequent algorithmic issues. For more on such issues, the interested reader should see [71, 30]. Our basic mental model of light is that of “geometric optics”. We think of light as a field of photons flying through space. In free space, each photon flies unmolested in a straight line, and each moves at the same speed.