Fast, High Quality Noise
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Perlin Textures in Real Time Using Opengl Antoine Miné, Fabrice Neyret
Perlin Textures in Real Time using OpenGL Antoine Miné, Fabrice Neyret To cite this version: Antoine Miné, Fabrice Neyret. Perlin Textures in Real Time using OpenGL. [Research Report] RR- 3713, INRIA. 1999, pp.18. inria-00072955 HAL Id: inria-00072955 https://hal.inria.fr/inria-00072955 Submitted on 24 May 2006 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE Perlin Textures in Real Time using OpenGL Antoine Mine´ Fabrice Neyret iMAGIS-IMAG, bat C BP 53, 38041 Grenoble Cedex 9, FRANCE [email protected] http://www-imagis.imag.fr/Membres/Fabrice.Neyret/ No 3713 juin 1999 THEME` 3 apport de recherche ISSN 0249-6399 Perlin Textures in Real Time using OpenGL Antoine Miné Fabrice Neyret iMAGIS-IMAG, bat C BP 53, 38041 Grenoble Cedex 9, FRANCE [email protected] http://www-imagis.imag.fr/Membres/Fabrice.Neyret/ Thème 3 — Interaction homme-machine, images, données, connaissances Projet iMAGIS Rapport de recherche n˚3713 — juin 1999 — 18 pages Abstract: Perlin’s procedural solid textures provide for high quality rendering of surface appearance like marble, wood or rock. -
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. -
Nuke Survival Toolkit Documentation
Nuke Survival Toolkit Documentation Release v1.0.0 Tony Lyons | 2020 1 About The Nuke Survival Toolkit is a portable tool menu for the Foundry’s Nuke with a hand-picked selection of nuke gizmos collected from all over the web, organized into 1 easy-to-install toolbar. Installation Here’s how to install and use the Nuke Survival Toolkit: 1.) Download the .zip folder from the Nuke Survival Toolkit github website. https://github.com/CreativeLyons/NukeSurvivalToolkit_publicRelease This github will have all of the up to date changes, bug fixes, tweaks, additions, etc. So feel free to watch or star the github, and check back regularly if you’d like to stay up to date. 2.) Copy or move the NukeSurvivalToolkit Folder either in your User/.nuke/ folder for personal use, or for use in a pipeline or to share with multiple artists, place the folder in any shared and accessible network folder. 3.) Open your init.py file in your /.nuke/ folder into any text editor (or create a new init.py in your User/.nuke/ directory if one doesn’t already exist) 4.) Copy the following code into your init.py file: nuke.pluginAddPath( "Your/NukeSurvivalToolkit/FolderPath/Here") 5.) Copy the file path location of where you placed your NukeSurvivalToolkit. Replace the Your/NukeSurvivalToolkit/FolderPath/Here text with your NukeSurvivalToolkit filepath location, making sure to keep quotation marks around the filepath. 6.) Save your init.py file, and restart your Nuke session 7.) That’s it! Congrats, you will now see a little red multi-tool in your nuke toolbar. -
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. -
IWCE 2015 PTIG-P25 Foundations Part 2
Sponsored by: Project 25 College of Technology Security Services Update & Vocoder & Range Improvements Bill Janky Director, System Design IWCE 2015, Las Vegas, Nevada March 16, 2015 Presented by: PTIG - The Project 25 Technology Interest Group www.project25.org – Booth 1853 © 2015 PTIG Agenda • Overview of P25 Security Services - Confidentiality - Integrity - Key Management • Current status of P25 security standards - Updates to existing services - New services 2 © 2015 PTIG PTIG - Project 25 Technology Interest Group IWCE 2015 I tell Fearless Leader we broke code. Moose and Squirrel are finished! 3 © 2015 PTIG PTIG - Project 25 Technology Interest Group IWCE 2015 Why do we need security? • Protecting information from security threats has become a vital function within LMR systems • What’s a threat? Threats are actions that a hypothetical adversary might take to affect some aspect of your system. Examples: – Message interception – Message replay – Spoofing – Misdirection – Jamming / Denial of Service – Traffic analysis – Subscriber duplication – Theft of service 4 © 2015 PTIG PTIG - Project 25 Technology Interest Group IWCE 2015 What P25 has for you… • The TIA-102 standard provides several standardized security services that have been adopted for implementation in P25 systems. • These security services may be used to provide security of information transferred across FDMA or TDMA P25 radio systems. Note: Most of the security services are optional and users must consider that when making procurements 5 © 2015 PTIG PTIG - Project 25 Technology -
Generating Realistic City Boundaries Using Two-Dimensional Perlin Noise
Generating realistic city boundaries using two-dimensional Perlin noise Graduation thesis for the Doctoraal program, Computer Science Steven Wijgerse student number 9706496 February 12, 2007 Graduation Committee dr. J. Zwiers dr. M. Poel prof.dr.ir. A. Nijholt ir. F. Kuijper (TNO Defence, Security and Safety) University of Twente Cluster: Human Media Interaction (HMI) Department of Electrical Engineering, Mathematics and Computer Science (EEMCS) Generating realistic city boundaries using two-dimensional Perlin noise Graduation thesis for the Doctoraal program, Computer Science by Steven Wijgerse, student number 9706496 February 12, 2007 Graduation Committee dr. J. Zwiers dr. M. Poel prof.dr.ir. A. Nijholt ir. F. Kuijper (TNO Defence, Security and Safety) University of Twente Cluster: Human Media Interaction (HMI) Department of Electrical Engineering, Mathematics and Computer Science (EEMCS) Abstract Currently, during the creation of a simulator that uses Virtual Reality, 3D content creation is by far the most time consuming step, of which a large part is done by hand. This is no different for the creation of virtual urban environments. In order to speed up this process, city generation systems are used. At re-lion, an overall design was specified in order to implement such a system, of which the first step is to automatically create realistic city boundaries. Within the scope of a research project for the University of Twente, an algorithm is proposed for this first step. This algorithm makes use of two-dimensional Perlin noise to fill a grid with random values. After applying a transformation function, to ensure a minimum amount of clustering, and a threshold mech- anism to the grid, the hull of the resulting shape is converted to a vector representation. -
CMSC 425: Lecture 14 Procedural Generation: Perlin Noise
CMSC 425 Dave Mount CMSC 425: Lecture 14 Procedural Generation: Perlin Noise Reading: The material on Perlin Noise based in part by the notes Perlin Noise, by Hugo Elias. (The link to his materials seems to have been lost.) This is not exactly the same as Perlin noise, but the principles are the same. Procedural Generation: Big game companies can hire armies of artists to create the immense content that make up the game's virtual world. If you are designing a game without such extensive resources, an attractive alternative for certain natural phenomena (such as terrains, trees, and atmospheric effects) is through the use of procedural generation. With the aid of a random number generator, a high quality procedural generation system can produce remarkably realistic models. Examples of such systems include terragen (see Fig. 1(a)) and speedtree (see Fig. 1(b)). terragen speedtree (a) (b) Fig. 1: (a) A terrain generated by terragen and (b) a scene with trees generated by speedtree. Before discussing methods for generating such interesting structures, we need to begin with a background, which is interesting in its own right. The question is how to construct random noise that has nice structural properties. In the 1980's, Ken Perlin came up with a powerful and general method for doing this (for which he won an Academy Award!). The technique is now widely referred to as Perlin Noise (see Fig. 2(a)). A terrain resulting from applying this is shown in Fig. 2(b). (The terragen software uses randomized methods like Perlin noise as a starting point for generating a terrain and then employs additional operations such as simulated erosion, to achieve heightened realism.) Perlin Noise: Natural phenomena derive their richness from random variations. -
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 -
State of the Art in Procedural Noise Functions
EUROGRAPHICS 2010 / Helwig Hauser and Erik Reinhard STAR – State of The Art Report State of the Art in Procedural Noise Functions A. Lagae1,2 S. Lefebvre2,3 R. Cook4 T. DeRose4 G. Drettakis2 D.S. Ebert5 J.P. Lewis6 K. Perlin7 M. Zwicker8 1Katholieke Universiteit Leuven 2REVES/INRIA Sophia-Antipolis 3ALICE/INRIA Nancy Grand-Est / Loria 4Pixar Animation Studios 5Purdue University 6Weta Digital 7New York University 8University of Bern Abstract Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This state-of-the-art report is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied on surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. -
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 -
Hardware-Accelerated Gradient Noise for Graphics
Hardware-Accelerated Gradient Noise for Graphics Josef B. Spjut Andrew E. Kensler Erik L. Brunvand School of Computing SCI Institute School of Computing University of Utah University of Utah University of Utah [email protected] [email protected] [email protected] ABSTRACT techniques trade computation for memory. This is impor- A synthetic noise function is a key component of most com- tant since as process technology scales, compute resources puter graphics rendering systems. This pseudo-random noise will increasingly outstrip memory speeds. For texturing sur- function is used to create a wide variety of natural looking faces, the memory reduction can be two-fold: first there textures that are applied to objects in the scene. To be is the simple reduction in texture memory itself. Second, useful, the generated noise should be repeatable while ex- 3D or \solid" procedural textures can eliminate the need for hibiting no discernible periodicity, anisotropy, or aliasing. explicit texture coordinates to be stored with the models. However, noise with these qualities is computationally ex- However, in order to avoid uniformity and produce visual pensive and results in a significant fraction of the run time richness, a simple, repeatable, pseudo-random function is for scenes with rich visual complexity. We propose modifi- required. Noise functions meet this need. Simply described, a noise function in computer graphics is cations to the standard algorithm for computing synthetic N noise that improve the visual quality of the noise, and a par- an R ! R mapping used to introduce irregularity into an allel hardware implementation of this improved noise func- otherwise regular pattern. -
Tile-Based Method for Procedural Content Generation
Tile-based Method for Procedural Content Generation Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By David Maung Graduate Program in Computer Science and Engineering The Ohio State University 2016 Dissertation Committee: Roger Crawfis, Advisor; Srinivasan Parthasarathy; Kannan Srinivasan; Ken Supowit Copyright by David Maung 2016 Abstract Procedural content generation for video games (PCGG) is a growing field due to its benefits of reducing development costs and adding replayability. While there are many different approaches to PCGG, I developed a body of research around tile-based approaches. Tiles are versatile and can be used for materials, 2D game content, or 3D game content. They may be seamless such that a game player cannot perceive that game content was created with tiles. Tile-based approaches allow localized content and semantics while being able to generate infinite worlds. Using techniques such as aperiodic tiling and spatially varying tiling, we can guarantee these infinite worlds are rich playable experiences. My research into tile-based PCGG has led to results in four areas: 1) development of a tile-based framework for PCGG, 2) development of tile-based bandwidth limited noise, 3) development of a complete tile-based game, and 4) application of formal languages to generation and evaluation models in PCGG. ii Vita 2009................................................................B.S. Computer Science, San Diego State