A Journey in a Procedural Volume Optimization and Filtering of Perlin Noise
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Seamscape a Procedural Generation System for Accelerated Creation of 3D Landscapes
SeamScape A procedural generation system for accelerated creation of 3D landscapes Bachelor Thesis in Computer Science and Engineering Link to video demonstration: youtu.be/K5yaTmksIOM Sebastian Ekman Anders Hansson Thomas Högberg Anna Nylander Alma Ottedag Joakim Thorén Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering Gothenburg, Sweden, June 2016 The Authors grants to Chalmers University of Technology and University of Gothenburg the non-exclusive right to publish the Work electronically and in a non-commercial purpose make it accessible on the Internet. The Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other material that violates copyright law. The Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let Chalmers University of Technology and University of Gothenburg store the Work electronically and make it accessible on the Internet. SeamScape A procedural generation system for accelerated creation of 3D landscapes Sebastian Ekman Anders Hansson Thomas Högberg Anna Nylander Alma Ottedag Joakim Thorén c Sebastian Ekman, 2016. c Anders Hansson, 2016. c Thomas Högberg, 2016. c Anna Nylander, 2016. c Alma Ottedag, 2016. -
Coping with the Bounds: a Neo-Clausewitzean Primer / Thomas J
About the CCRP The Command and Control Research Program (CCRP) has the mission of improving DoD’s understanding of the national security implications of the Information Age. Focusing upon improving both the state of the art and the state of the practice of command and control, the CCRP helps DoD take full advantage of the opportunities afforded by emerging technologies. The CCRP pursues a broad program of research and analysis in information superiority, information operations, command and control theory, and associated operational concepts that enable us to leverage shared awareness to improve the effectiveness and efficiency of assigned missions. An important aspect of the CCRP program is its ability to serve as a bridge between the operational, technical, analytical, and educational communities. The CCRP provides leadership for the command and control research community by: • articulating critical research issues; • working to strengthen command and control research infrastructure; • sponsoring a series of workshops and symposia; • serving as a clearing house for command and control related research funding; and • disseminating outreach initiatives that include the CCRP Publication Series. This is a continuation in the series of publications produced by the Center for Advanced Concepts and Technology (ACT), which was created as a “skunk works” with funding provided by the CCRP under the auspices of the Assistant Secretary of Defense (NII). This program has demonstrated the importance of having a research program focused on the national security implications of the Information Age. It develops the theoretical foundations to provide DoD with information superiority and highlights the importance of active outreach and dissemination initiatives designed to acquaint senior military personnel and civilians with these emerging issues. -
Procedural Generation of a 3D Terrain Model Based on a Predefined
Procedural Generation of a 3D Terrain Model Based on a Predefined Road Mesh Bachelor of Science Thesis in Applied Information Technology Matilda Andersson Kim Berger Fredrik Burhöi Bengtsson Bjarne Gelotte Jonas Graul Sagdahl Sebastian Kvarnström Department of Applied Information Technology Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2017 Bachelor of Science Thesis Procedural Generation of a 3D Terrain Model Based on a Predefined Road Mesh Matilda Andersson Kim Berger Fredrik Burhöi Bengtsson Bjarne Gelotte Jonas Graul Sagdahl Sebastian Kvarnström Department of Applied Information Technology Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2017 The Authors grants to Chalmers University of Technology and University of Gothenburg the non-exclusive right to publish the Work electronically and in a non-commercial purpose make it accessible on the Internet. The Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other material that violates copyright law. The Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let Chalmers University of Technology and University of Gothenburg store the Work electronically and make it accessible on the Internet. Procedural Generation of a 3D Terrain Model Based on a Predefined Road Mesh Matilda Andersson Kim Berger Fredrik Burhöi Bengtsson Bjarne Gelotte Jonas Graul Sagdahl Sebastian Kvarnström © Matilda Andersson, 2017. -
Annual Report 2018
2018 Annual Report 4 A Message from the Chair 5 A Message from the Director & President 6 Remembering Keith L. Sachs 10 Collecting 16 Exhibiting & Conserving 22 Learning & Interpreting 26 Connecting & Collaborating 30 Building 34 Supporting 38 Volunteering & Staffing 42 Report of the Chief Financial Officer Front cover: The Philadelphia Assembled exhibition joined art and civic engagement. Initiated by artist Jeanne van Heeswijk and shaped by hundreds of collaborators, it told a story of radical community building and active resistance; this spread, clockwise from top left: 6 Keith L. Sachs (photograph by Elizabeth Leitzell); Blocks, Strips, Strings, and Half Squares, 2005, by Mary Lee Bendolph (Purchased with the Phoebe W. Haas fund for Costume and Textiles, and gift of the Souls Grown Deep Foundation from the William S. Arnett Collection, 2017-229-23); Delphi Art Club students at Traction Company; Rubens Peale’s From Nature in the Garden (1856) was among the works displayed at the 2018 Philadelphia Antiques and Art Show; the North Vaulted Walkway will open in spring 2019 (architectural rendering by Gehry Partners, LLP and KXL); back cover: Schleissheim (detail), 1881, by J. Frank Currier (Purchased with funds contributed by Dr. Salvatore 10 22 M. Valenti, 2017-151-1) 30 34 A Message from the Chair A Message from the As I observe the progress of our Core Project, I am keenly aware of the enormity of the undertaking and its importance to the Museum’s future. Director & President It will be transformative. It will not only expand our exhibition space, but also enhance our opportunities for community outreach. -
Fast, High Quality Noise
The Importance of Being Noisy: Fast, High Quality Noise Natalya Tatarchuk 3D Application Research Group AMD Graphics Products Group Outline Introduction: procedural techniques and noise Properties of ideal noise primitive Lattice Noise Types Noise Summation Techniques Reducing artifacts General strategies Antialiasing Snow accumulation and terrain generation Conclusion Outline Introduction: procedural techniques and noise Properties of ideal noise primitive Noise in real-time using Direct3D API Lattice Noise Types Noise Summation Techniques Reducing artifacts General strategies Antialiasing Snow accumulation and terrain generation Conclusion The Importance of Being Noisy Almost all procedural generation uses some form of noise If image is food, then noise is salt – adds distinct “flavor” Break the monotony of patterns!! Natural scenes and textures Terrain / Clouds / fire / marble / wood / fluids Noise is often used for not-so-obvious textures to vary the resulting image Even for such structured textures as bricks, we often add noise to make the patterns less distinguishable Ех: ToyShop brick walls and cobblestones Why Do We Care About Procedural Generation? Recent and upcoming games display giant, rich, complex worlds Varied art assets (images and geometry) are difficult and time-consuming to generate Procedural generation allows creation of many such assets with subtle tweaks of parameters Memory-limited systems can benefit greatly from procedural texturing Smaller distribution size Lots of variation -
Synthetic Data Generation for Deep Learning Models
32. DfX-Symposium 2021 Synthetic Data Generation for Deep Learning Models Christoph Petroll 1 , 2 , Martin Denk 2 , Jens Holtmannspötter 1 ,2, Kristin Paetzold 3 , Philipp Höfer 2 1 The Bundeswehr Research Institute for Materials, Fuels and Lubricants (WIWeB) 2 Universität der Bundeswehr München (UniBwM) 3 Technische Universität Dresden * Korrespondierender Autor: Christoph Petroll Institutsweg 1 85435 Erding Germany Telephone: 08122/9590 3313 Mail: [email protected] Abstract The design freedom and functional integration of additive manufacturing is increasingly being implemented in existing products. One of the biggest challenges are competing optimization goals and functions. This leads to multidisciplinary optimization problems which needs to be solved in parallel. To solve this problem, the authors require a synthetic data set to train a deep learning metamodel. The research presented shows how to create a data set with the right quality and quantity. It is discussed what are the requirements for solving an MDO problem with a metamodel taking into account functional and production-specific boundary conditions. A data set of generic designs is then generated and validated. The generation of the generic design proposals is accompanied by a specific product development example of a drone combustion engine. Keywords Multidisciplinary Optimization Problem, Synthetic Data, Deep Learning © 2021 die Autoren | DOI: https://doi.org/10.35199/dfx2021.11 1. Introduction and Idea of This Research Due to its great design freedom, additive manufacturing (AM) shows a high potential of functional integration and part consolidation [1]-[3]. For this purpose, functions and optimization goals that are usually fulfilled by individual components must be considered in parallel. -
Digital to Analog, PWM, Stepper Motors
Lecture #19 Digital To Analog, PWM, Stepper Motors 18-348 Embedded System Engineering Philip Koopman Monday, 28-March-2016 Electrical& Computer ENGINEERING © Copyright 2006-2016, Philip Koopman, All Rights Reserved Example System: HVAC Compressor Expansion Valve [http://www.solarpowerwindenergy.org] 2 HVAC Embedded Control Compressors (reciprocating & scroll) • Smart loading and unloading of compressor – Want to minimize motor turn on/turn off cycles – May involve bypassing liquid so compressor keeps running but doesn’t compress • Variable speed for better output temperature control • Diagnostics and prognostics – Prevent equipment damage (e.g., liquid entering compressor, compressor stall) – Predict equipment failures (e.g., low refrigerant, motor bearings wearing out) Expansion Valve • Smart control of amount of refrigerant evaporated – Often a stepper motor • Diagnostics and prognostics – Low refrigerant, icing on cold coils, overheating of hot coils System coordination • Coordinate expansion valve and compressor operation • Coordinate multiple compressors • Next lecture – talk about building-level system level diagnostics & coordination 3 Where Are We Now? Where we’ve been: • Interrupts, concurrency, scheduling, RTOS Where we’re going today: • Analog Output Where we’re going next: • Analog Input •Human I/O • Very gentle introduction to control •… • Test #2 and last project demo 4 Preview Digital To Analog Conversion • Example implementation • Understanding performance • Low pass filters Waveform encoding PWM • Digital way -
Networking, Noise
Final TAs • Jeff – Ben, Carmen, Audrey • Tyler – Ruiqi – Mason • Ben – Loudon – Jordan, Nick • David – Trent – Vijay Final Design Checks • Optional this week • Required for Final III Playtesting Feedback • Need three people to playtest your game – One will be in class – Two will be out of class (friends, SunLab, etc.) • Should watch your victim volunteer play your game – Don’t comment unless necessary – Take notes – Pay special attention to when the player is lost and when the player is having the most fun Networking STRATEGIES The Illusion • All players are playing in real-time on the same machine • This isn’t possible • We need to emulate this as much as possible The Illusion • What the player should see: – Consistent game state – Responsive controls – Difficult to cheat • Things working against us: – Game state > bandwidth – Variable or high latency – Antagonistic users Send the Entire World! • Players take turns modifying the game world and pass it back and forth • Works alright for turn-based games • …but usually it’s bad – RTS: there are a million units – FPS: there are a million bullets – Fighter: timing is crucial Modeling the World • If we’re sending everything, we’re modeling the world as if everything is equally important Player 1 Player 2 – But it really isn’t! My turn Not my turn Read-only – Composed of entities, only World some of which react to the StateWorld 1 player • We need a better model to solve these problems Client-Server Model • One process is the authoritative server Player 1 (server) Player 2 (client) – Now we -
Prime Gradient Noise
Computational Visual Media https://doi.org/10.1007/s41095-021-0206-z Research Article Prime gradient noise Sheldon Taylor1,∗, Owen Sharpe1,∗, and Jiju Peethambaran1 ( ) c The Author(s) 2021. Abstract Procedural noise functions are fundamental 1 Introduction tools in computer graphics used for synthesizing virtual geometry and texture patterns. Ideally, a Visually pleasing 3D content is one of the core procedural noise function should be compact, aperiodic, ingredients of successful movies, video games, and parameterized, and randomly accessible. Traditional virtual reality applications. Unfortunately, creation lattice noise functions such as Perlin noise, however, of high quality virtual 3D content and textures exhibit periodicity due to the axial correlation induced is a labour-intensive task, often requiring several while hashing the lattice vertices to the gradients. months to years of skilled manual labour. A In this paper, we introduce a parameterized lattice relatively cheap yet powerful alternative to manual noise called prime gradient noise (PGN) that minimizes modeling is procedural content generation using a discernible periodicity in the noise while enhancing the set of rules, such as L-systems [1] or procedural algorithmic efficiency. PGN utilizes prime gradients, a noise [2]. Procedural noise has proven highly set of random unit vectors constructed from subsets of successful in creating computer generated imagery prime numbers plotted in polar coordinate system. To (CGI) consisting of 3D models exhibiting fine detail map axial indices of lattice vertices to prime gradients, at multiple scales. Furthermore, procedural noise PGN employs Szudzik pairing, a bijection F : N2 → N. is a compact, flexible, and low cost computational Compositions of Szudzik pairing functions are used in technique to synthesize a range of patterns that may higher dimensions. -
Procedural Generation of Infinite Terrain from Real-World Elevation
Journal of Computer Graphics Techniques Vol. 3, No. 1, 2014 http://jcgt.org Designer Worlds: Procedural Generation of Infinite Terrain from Real-World Elevation Data Ian Parberry University of North Texas Figure 1.A 180◦ panorama of terrain generated using elevation data from Utah. Abstract The standard way to procedurally generate random terrain for video games and other applica- tions is to post-process the output of a fast noise generator such as Perlin noise. Tuning the post-processing to achieve particular types of terrain requires game designers to be reason- ably well-trained in mathematics. A well-known variant of Perlin noise called value noise is used in a process accessible to designers trained in geography to generate geotypical terrain based on elevation statistics drawn from widely available sources such as the United States Geographical Service. A step-by-step process for downloading and creating terrain from real- world USGS elevation data is described, and an implementation in C++ is given. 1. Introduction Terrain generation is an example of what is known in the game industry as procedu- ral content generation. Procedural content generation needs to have three important properties. First, it needs to be fast, meaning that it has to use only a fraction of the computing power on a current-generation computer. Second, it needs to be both ran- dom and structured so that it creates content that is varied and interesting. Third, it needs to be controllable in a natural and intuitive way. As noted in the excellent survey paper by Smelik et al. [2009], procedural terrain generation is often based on fractal noise generators such as Perlin noise [Perlin 1985; Perlin 2002] (for more details see, for example, the book by Ebert et al. -
Coherent Noise
Coherent Noise User manual Table of Contents Introduction...........................................................................................5 Chapter I. .............................................................................................6 Noise Basics........................................................................................6 Combining Noise. ................................................................................8 CoherentNoise library...........................................................................9 Chapter II............................................................................................12 Noise generators................................................................................12 Generator base class.......................................................................12 ValueNoise.....................................................................................13 GradientNoise................................................................................14 ValueNoise2D and GradientNoise2D...................................................16 Constant.......................................................................................16 Function........................................................................................17 Cache...........................................................................................17 Patterns........................................................................................18 Cylinders....................................................................................18 -
Ray Tracing a Tiny Procedural Planet Morgan Mcguire University of Waterloo & Nvidia @Casualeffects
RAY TRACING A TINY PROCEDURAL PLANET MORGAN MCGUIRE UNIVERSITY OF WATERLOO & NVIDIA @CASUALEFFECTS I’ll begin with an example of coding a real procedural content generator in 2D from scratch, before circling back to talk about the why, what, and how of “procgen” at a high level. From there we’ll look at ray tracing and implicit surfaces and end up by creating a 3D model of a cute little planet. At the end you’ll have resources and ideas to start your own experiments in this space. 1 CREATING A UNIVERSE FROM RULES A lot of graphics content development is done by artists, programmers, and writers placing each piece of their virtual universe and painting, animating, scripting, etc. it by hand. The alternative is to not create anything except the laws of physics for your universe, and then letting the laws and some initial conditions create all of the richness. That’s procedural generation. Watch me create such a universe right now: 2 (1, 1) (0, 0) In the beginning, we have formless darkness. That’s how every graphics program starts, with this black screen. In my coordinate system, the origin is the lower-left and the upper right corner is (1, 1) 3 void mainImage(out Color color, in Point coord) { float x = pixel.x / iResolution.x; float y = pixel.y / iResolution.y; color = Color(0.0); } Here’s the code for a GPU pixel program that draws the black screen. If you’re not used to writing graphics code, then I just have to tell you a few simple things to understand this: - “float” is a 32-bit real number.