POV-Ray Reference

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POV-Ray Reference POV-Team for POV-Ray Version 3.6.1 ii Contents 1 Introduction 1 1.1 Notation and Basic Assumptions . 1 1.2 Command-line Options . 2 1.2.1 Animation Options . 3 1.2.2 General Output Options . 6 1.2.3 Display Output Options . 8 1.2.4 File Output Options . 11 1.2.5 Scene Parsing Options . 14 1.2.6 Shell-out to Operating System . 16 1.2.7 Text Output . 20 1.2.8 Tracing Options . 23 2 Scene Description Language 29 2.1 Language Basics . 29 2.1.1 Identifiers and Keywords . 30 2.1.2 Comments . 34 2.1.3 Float Expressions . 35 2.1.4 Vector Expressions . 43 2.1.5 Specifying Colors . 48 2.1.6 User-Defined Functions . 53 2.1.7 Strings . 58 2.1.8 Array Identifiers . 60 2.1.9 Spline Identifiers . 62 2.2 Language Directives . 64 2.2.1 Include Files and the #include Directive . 64 2.2.2 The #declare and #local Directives . 65 2.2.3 File I/O Directives . 68 2.2.4 The #default Directive . 70 2.2.5 The #version Directive . 71 2.2.6 Conditional Directives . 72 2.2.7 User Message Directives . 75 2.2.8 User Defined Macros . 76 3 Scene Settings 81 3.1 Camera . 81 3.1.1 Placing the Camera . 82 3.1.2 Types of Projection . 86 3.1.3 Focal Blur . 88 3.1.4 Camera Ray Perturbation . 89 3.1.5 Camera Identifiers . 89 3.2 Atmospheric Effects . 90 iv CONTENTS 3.2.1 Atmospheric Media . 90 3.2.2 Background . 90 3.2.3 Fog . 91 3.2.4 Sky Sphere . 92 3.2.5 Rainbow . 93 3.3 Global Settings . 94 3.3.1 ADC Bailout . 95 3.3.2 Ambient Light . 95 3.3.3 Assumed Gamma . 96 3.3.4 HF Gray 16...................................... 98 3.3.5 Irid Wavelength . 98 3.3.6 Charset . 99 3.3.7 Max Trace Level................................... 99 3.3.8 Max Intersections . 100 3.3.9 Number Of Waves.................................. 100 3.3.10 Noise generator . 100 3.3.11 Radiosity Basics . 100 3.4 Radiosity . 101 3.4.1 How Radiosity Works . 101 3.4.2 Adjusting Radiosity . 101 3.4.3 Tips on Radiosity . 104 4 Objects 107 4.1 Finite Solid Primitives . 108 4.1.1 Blob . 108 4.1.2 Box . 110 4.1.3 Cone . 111 4.1.4 Cylinder . 112 4.1.5 Height Field . 112 4.1.6 Julia Fractal . 115 4.1.7 Lathe . 117 4.1.8 Prism . 119 4.1.9 Sphere . 121 4.1.10 Sphere Sweep . 121 4.1.11 Superquadric Ellipsoid . 122 4.1.12 Surface of Revolution . 123 4.1.13 Text . 125 4.1.14 Torus . 126 4.2 Finite Patch Primitives . 126 4.2.1 Bicubic Patch . 127 4.2.2 Disc . 128 4.2.3 Mesh . 129 4.2.4 Mesh2 . 130 4.2.5 Polygon . 131 4.2.6 Triangle and Smooth Triangle . 133 4.3 Infinite Solid Primitives . 133 4.3.1 Plane . 134 4.3.2 Poly, Cubic and Quartic . 134 4.3.3 Quadric . 137 4.4 Isosurface Object . 137 4.5 Parametric Object . 139 4.6 Constructive Solid Geometry . 140 4.6.1 Inside and Outside . 141 CONTENTS v 4.6.2 Union . 141 4.6.3 Intersection . 143 4.6.4 Difference . 143 4.6.5 Merge . 144 4.7 Light Sources . ..

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Photon Mapping Assignment
Photon Mapping Assignment 15-864 Advanced Computer Graphics, Carnegie Mellon University Instructor: Doug L. James TA: Christopher Twigg Introduction sampling to emit photons of equal intensity from the diffuse area light source. Use the ray tracer’s functionality to propagate photons In this assignment you will implement (portions of) a photon map- (reflect, transmit and absorb) throughout the scene. To maintain ping renderer. For simplicity, we will only consider scenes with a photons of similar intensity, use Russian roulette [Arvo and Kirk single area light source, and assume surfaces are diffuse, or purely 1990] to determine if photons are absorbed (diffuse), transmitted specular (e.g., mirror or glass). To generate images for testing and (transparent), or reflected at surfaces. Use Schlick’s approximation grading, a test scene will be provided for you on the class website; to Fresnel’s specular reflection coefficient to determine the prob- this will be a very simple consisting of the Cornell box, an area ability of transmission and reflection at specular interfaces, e.g., light source, and specular spheres. Although a brief explanation of glass. Store the photons in the photon map using Jensen’s kd-tree what needs to be done is given below, further implementation de- data structure implementation (provided on the web page). Once tails can be found in [Jensen 2001; Jensen 1996], as well as other these photons are stored, the data structure can compute the filtered ray tracing [Shirley 2000], Monte Carlo [Jensen 2003], and global irradiance estimates you need later. illumination texts [Dutre´ et al. 2003]. Build the Caustic Photon Map (20 points): The high- Getting Started: Familiarize yourself with resolution caustic photon map represents the LS+D paths, and it the ray tracer is therefore only necessary to emit photons toward specular objects when computing the caustic photon map.
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Seamless Texture Mapping of 3D Point Clouds
Seamless Texture Mapping of 3D Point Clouds Dan Goldberg Mentor: Carl Salvaggio Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology Rochester, NY November 25, 2014 Abstract The two similar, quickly growing fields of computer vision and computer graphics give users the ability to immerse themselves in a realistic computer generated environment by combining the ability create a 3D scene from images and the texture mapping process of computer graphics. The output of a popular computer vision algorithm, structure from motion (obtain a 3D point cloud from images) is incomplete from a computer graphics standpoint. The final product should be a textured mesh. The goal of this project is to make the most aesthetically pleasing output scene. In order to achieve this, auxiliary information from the structure from motion process was used to texture map a meshed 3D structure. 1 Introduction The overall goal of this project is to create a textured 3D computer model from images of an object or scene. This problem combines two different yet similar areas of study. Computer graphics and computer vision are two quickly growing fields that take advantage of the ever-expanding abilities of our computer hardware. Computer vision focuses on a computer capturing and understanding the world. Computer graphics con- centrates on accurately representing and displaying scenes to a human user. In the computer vision field, constructing three-dimensional (3D) data sets from images is becoming more common. Microsoft's Photo- synth (Snavely et al., 2006) is one application which brought attention to the 3D scene reconstruction field. Many structure from motion algorithms are being applied to data sets of images in order to obtain a 3D point cloud (Koenderink and van Doorn, 1991; Mohr et al., 1993; Snavely et al., 2006; Crandall et al., 2011; Weng et al., 2012; Yu and Gallup, 2014; Agisoft, 2014).
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Poseray Handbuch 3.10.3
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Real-Time Global Illumination with Photon Mapping Niklas Smal and Maksim Aizenshtein UL Benchmarks
CHAPTER 24 Real-Time Global Illumination with Photon Mapping Niklas Smal and Maksim Aizenshtein UL Benchmarks ABSTRACT Indirect lighting, also known as global illumination, is a crucial effect in photorealistic images. While there are a number of effective global illumination techniques based on precomputation that work well with static scenes, including global illumination for scenes with dynamic lighting and dynamic geometry remains a challenging problem. In this chapter, we describe a real-time global illumination algorithm based on photon mapping that evaluates several bounces of indirect lighting without any precomputed data in scenes with both dynamic lighting and fully dynamic geometry. We explain both the pre- and post-processing steps required to achieve dynamic high-quality illumination within the limits of a real- time frame budget. 24.1 INTRODUCTION As the scope of what is possible with real-time graphics has grown with the advancing capabilities of graphics hardware, scenes have become increasingly complex and dynamic. However, most of the current real-time global illumination algorithms (e.g., light maps and light probes) do not work well with moving lights and geometry due to these methods’ dependence on precomputed data. In this chapter, we describe an approach based on an implementation of photon mapping [7], a Monte Carlo method that approximates lighting by first tracing paths of light-carrying photons in the scene to create a data structure that represents the indirect illumination and then using that structure to estimate indirect light at points being shaded. See Figure 24-1. Photon mapping has a number of useful properties, including that it is compatible with precomputed global illumination, provides a result with similar quality to current static techniques, can easily trade off quality and computation time, and requires no significant artist work.
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Efficient Rendering of Caustics with Streamed Photon Mapping
BULLETIN OF THE POLISH ACADEMY OF SCIENCES TECHNICAL SCIENCES, Vol. 65, No. 3, 2017 DOI: 10.1515/bpasts-2017-0040 Efficient rendering of caustics with streamed photon mapping K. GUZEK and P. NAPIERALSKI* Institute of Information Technology, Lodz University of Technology, 215 Wolczanska St., 90-924 Lodz, Poland Abstract. In this paper, we present the streamed photon mapping method for enhancing the rendering of caustics. In order to achieve a realistic caustic effect, global illumination methods require additional data, which are gathered by creating a caustic map or increasing the number of samples used for rendering. Our method employs a stream of photons with a varying luminance level depending on the material properties of the surface. The application of a concentrated photon stream provides the ability to render caustics effectively without increasing the number of photons in a photon map. Such an approach increases visibility of results, while also allowing for faster computations. Key words: rendering, global illumination, photon mapping, caustics. 1. Introduction 2. Rendering caustics The interaction of light with matter in the real world results The first attempt to simulate a natural caustic effect was the in a variety of optical phenomena. Understanding how those path tracing method, introduced by James Kajiya in 1986 [4]. phenomena occur and where to implement them is crucial for However, the method proved to be highly inefficient. The creating realistic image renders. When observing the reflection caustics were poorly rendered, as the light source was obscure. or refraction of light through curved surfaces, one may notice A significant improvement was introduced both and inde- some characteristic patches of light, referred to as caustics.
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FACTA UNIVERSITATIS UNIVERSITY of NIŠ ISSN 0354-4605 (Print) ISSN 2406-0860 (Online) Series Architecture and Civil Engineering COBISS.SR-ID 98807559 Vol
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Texture Mapping There Are Limits to Geometric Modeling
Texture Mapping There are limits to geometric modeling http://www.beinteriordecorator.com National Geographic Although modern GPUs can render millions of triangles/sec, that’s not enough sometimes... Use texture mapping to increase realism through detail Rosalee Wolfe This image is just 8 polygons! [Angel and Shreiner] [Angel and No texture With texture Pixar - Toy Story Store 2D images in buffers and lookup pixel reflectances procedural photo Other uses of textures... [Angel and Shreiner] [Angel and Light maps Shadow maps Environment maps Bump maps Opacity maps Animation 99] [Stam Texture mapping in the OpenGL pipeline • Geometry and pixels have separate paths through pipeline • meet in fragment processing - where textures are applied • texture mapping applied at end of pipeline - efficient since relatively few polygons get past clipper uv Mapping Tschmits Wikimedia Commons • 2D texture is parameterized by (u,v) • Assign polygon vertices texture coordinates • Interpolate within polygon Texture Calibration Cylindrical mapping (x,y,z) -> (theta, h) -> (u,v) [Rosalee Wolfe] Spherical Mapping (x,y,z) -> (latitude,longitude) -> (u,v) [Rosalee Wolfe] Box Mapping [Rosalee Wolfe] Parametric Surfaces 32 parametric patches 3D solid textures [Dong et al., 2008] al., et [Dong can map object (x,y,z) directly to texture (u,v,w) Procedural textures Rosalee Wolfe e.g., Perlin noise Triangles Texturing triangles • Store (u,v) at each vertex • interpolate inside triangles using barycentric coordinates Texture Space Object Space v 1, 1 (u,v) = (0.2, 0.8)
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What You Seam Is What You Get: Automatic and Interactive UV Unwrapping
Online Submission ID: 0270 What you seam is what you get: automatic and interactive UV unwrapping Figure 1: Left: from a meshed model, our system automatically proposes an initial set of seams (black lines) and a valid texture mapping that the user starts with. Right: the user can interactively improve the mapping, by sewing charts and constraining seams (blue lines). As shown in the video, each user interaction is systematically echoed with instant visual feedback. Abstract • (2) Parameterization: each chart in 3-space is put into one to one 2 correspondence with a subset of R ; 3D paint systems opened the door to new texturing tools, directly operating on 3D objects. However, although time and effort was • (3) Packing: the charts are arranged in texture space to minimize devoted to mesh parameterization, UV unwrapping is still known storage requirements. to be a tedious and time-consuming process in Computer Graphics Time and effort was devoted to the problem of mesh parameteriza- production. We think that this is mainly due to the lack of well- tion (see e.g. SIGGRAPH course notes [Hormann et al. 2007] for adapted segmentation method. To make UV unwrapping easier, we an overview). Such mesh parameterization techniques are now well propose a new system, based on three components : • A novel spectral segmentation method that proposes reasonable known and broadly used in the Computer Graphics industry. Re- initial seams to the user; cently, formalizing the relations between deformations and curva- ture lead to both efﬁcient and provably correct methods [Ben-Chen • Several tools to edit and constrain the seams.
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2014 3-4 Acta Graphica.Indd
Vidmar et al.: Performance Assessment of Three Rendering..., acta graphica 25(2014)3–4, 101–114 author viewpoint acta graphica 234 Performance Assessment of Three Rendering Engines in 3D Computer Graphics Software Authors Žan Vidmar, Aleš Hladnik, Helena Gabrijelčič Tomc* University of Ljubljana Faculty of Natural Sciences and Engineering Slovenia *E-mail: [email protected] Abstract: The aim of the research was the determination of testing conditions and visual and numerical evaluation of renderings made with three different rendering engines in Maya software, which is widely used for educational and computer art purposes. In the theoretical part the overview of light phenomena and their simulation in virtual space is presented. This is followed by a detailed presentation of the main rendering methods and the results and limitations of their applications to 3D ob- jects. At the end of the theoretical part the importance of a proper testing scene and especially the role of Cornell box are explained. In the experimental part the terms and conditions as well as hardware and software used for the research are presented. This is followed by a description of the procedures, where we focused on the rendering quality and time, which enabled the comparison of settings of different render engines and determination of conditions for further rendering of testing scenes. The experimental part continued with rendering a variety of simple virtual scenes including Cornell box and virtual object with different materials and colours. Apart from visual evaluation, which was the starting point for comparison of renderings, a procedure for numerical estimation and colour deviations of ren- derings using the selected regions of interest in the final images is presented.
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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.
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The Beam Radiance Estimate for Volumetric Photon Mapping
The Beam Radiance Estimate for Volumetric Photon Mapping Wojciech Jarosz, Matthias Zwicker, and Henrik Wann Jensen University of California, San Diego Abstract We present a new method for efficiently simulating the scattering of light within participating media. Using a theoretical reformulation of volumetric photon mapping, we develop a novel photon gathering technique for participating media. Traditional volumetric photon mapping samples the in-scattered radiance at numerous points along the length of a single ray by performing costly range queries within the photon map. Our technique replaces these multiple point-queries with a single beam-query, which explicitly gathers all photons along the length of an entire ray. These photons are used to estimate the accumulated in-scattered radiance arriving from a particular direction and need to be gathered only once per ray. Our method handles both fixed and adaptive kernels, is faster than regular volumetric photon mapping, and produces images with less noise. Keywords: participating media, light transport, global illumination, rendering, photon tracing, photon map, ray marching, nearest neighbor, variable kernel method. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: raytracing; color, shading, shadowing, and texture; I.6.8 [Simulation and Modeling]: Monte Carlo; G.1.9 [Numerical Analysis]: Fredholm equations; integro-differential equations. 1. Introduction these approaches is that they suffer from noise that can only The appearance of many natural phenomena, such as human be overcome with a huge computational effort. skin, clouds, fire, water, or the atmosphere, are strongly in- One strategy to solve this issue is to make simplifying as- fluenced by the interaction of light with volumetric media.
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