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"Rendering (Computer graphics)"
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Physically Based Rendering
Physically Based Rendering, Second Edition, describes both the mathematical theory behind a modern photorealistic rendering system as well as its practical implementation.A method known as literate programming combines human-readable documentation and source code into a single reference that is specifically designed to aid comprehension.
eBook
Essential Computer Graphics Techniques for Modeling, Animating, and Rendering Biomolecules and Cells
The book helps readers develop fundamental skills in the field of biomedical illustrations with a training approach based on step-by-step tutorials with a practical approach. Medical/scientific illustration mainly belongs to professionals in the art field or scientists trying to create artistic visualization. There is not a merging between the two, even if the demand is high. This leads to accurate scientific images with no appeal (or trivial mistakes), or appealing images with huge scientific mistakes. This gives the fundamentals to the scientist so they can apply CG techniques that give a more scientific approach creating mistake-free images.
Key Features
This book provides a reference where none exist.
Without overwhelming the reader with software details it teaches basic principles to give readers to fundamentals to create.
Demonstrates professional artistic tools used by scientists to create better images for their work.
Coverage of lighting and rendering geared specifically for scientific work that is toturoal based with a practical approach.
Included are chapter tutorials, key terms and end of chapter references for Art and Scientific References for each chapter.
CHAPTER 1 ■ Preface CHAPTER 2 ■ Introduction CHAPTER 3 ■ Foundations CHAPTER 4 ■ Modeling and Lighting CHAPTER 5 ■ Scene Setup CHAPTER 6 ■ Rendering CHAPTER 7 ■ Animation CHAPTER 8 ■ Final Look CHAPTER 9 ■ Professional Practices
Giorgio Luciano is a scientist with a variety of interests. He currently works as a researcher at the Italian National Council of Research (CNR) on topics devoted to the characterization and development of new materials. The main topics of his research are calorimetry, rheometry surfaces and data analysis applied to material science. During his PhD he spent some time studying at the Academy of Fine Arts in Vienna, and this influenced his passion for art. He has been usin CG software for more that 20 years and has been a CGTALK member since 2005. His interests also focus on lighting and rendering, he currently manages the lighting challenges on the CGSociety Forums. You can find his work (architecture, design, and scientific visualization) at giorgioluciano.co.place
eBook
VR Developer Gems
This book takes the practicality of other \"Gems\" series such as \"Graphics Gems\" and \"Game Programming Gems\" and provide a quick reference for novice and expert programmers alike to swiftly track down a solution to a task needed for their VR project. Reading the book from cover to cover is not the expected use case, but being familiar with the territory from the Introduction and then jumping to the needed explanations is how the book will mostly be used. Each chapter (other than Introduction) will contain between 5 to 10 \"tips\", each of which is a self-contained explanation with implementation detail generally demonstrated as pseudo code, or in cases where it makes sense, actual code.
Key Features
Sections written by veteran virtual reality researchers and developers
Usable code snipits that readers can put to immediate use in their own projects.
Tips of value both to readers entering the field as well as those looking for solutions that expand their repertoire.
Chapter 1: Introduction. Chapter 2: 2. Extending existing renderers for VR. Chapter 3: User interfaces for Smartphone- VR. Hapetr 4: Navigating through the virtual world. Chapter 5: Image warping & blending. Chapter 6: Real-time rendering techniques for VR. Chapter 7: Foolimng the user. Chapter 8: VR on a Raspberry Pi:
\"This is a highly valuable resource, chiefly for developers with considerable technical background in computer graphics and/or programming.\"
- J. Brzezinski, McHenry County College, CHOICE Reviews , Highly recommended
William Sherman , Sr. Technology Advisor Indiana University Research Technologies
● Scientific Visualization lead, visualization production projects, visualization software development.
● Immersive applications, development and installation of immersive (virtual reality) tools and applications, education on immersive techniques and technologies.
● Development of relationship with the Idaho National Laboratory and other Department of Energy facilities.
● Proposal development, lead collaborative grant writing projects involving several universities.
● Open recipe hardware development, developing and building community for low-cost immersive displays (iq- station.org).
eBook
Recent advances in 3D Gaussian splatting
The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel view synthesis. Unlike neural implicit representations like neural radiance fields (NeRFs) that represent a 3D scene with position and viewpoint-conditioned neural networks, 3D Gaussian splatting utilizes a set of Gaussian ellipsoids to model the scene so that efficient rendering can be accomplished by rasterizing Gaussian ellipsoids into images. Apart from fast rendering, the explicit representation of 3D Gaussian splatting also facilitates downstream tasks like dynamic reconstruction, geometry editing, and physical simulation. Considering the rapid changes and growing number of works in this field, we present a literature review of recent 3D Gaussian splatting methods, which can be roughly classified by functionality into 3D reconstruction, 3D editing, and other downstream applications. Traditional point-based rendering methods and the rendering formulation of 3D Gaussian splatting are also covered to aid understanding of this technique. This survey aims to help beginners to quickly get started in this field and to provide experienced researchers with a comprehensive overview, aiming to stimulate future development of the 3D Gaussian splatting representation.
Journal Article
Dataset for classification of computer graphic images and photographic images
The recent advancements in computer graphics (CG) image rendering techniques have made it easy for the content creators to produce high quality computer graphics similar to photographic images (PG) confounding the most naïve users. Such images used with negative intent, cause serious problems to the society. In such cases, proving the authenticity of an image is a big challenge in digital image forensics due to high photo-realism of CG images. Existing datasets used to assess the performance of classification models are lacking with: (i) larger dataset size, (ii) diversified image contents, and (iii) images generated with the recent digital image rendering techniques. To fill this gap, we created two new datasets, namely, ‘JSSSTU CG and PG image dataset’ and ‘JSSSTU PRCG image dataset’. Further, the complexity of the new datasets and benchmark datasets are evaluated using handcrafted texture feature descriptors such as gray level co-occurrence matrix, local binary pattern and VGG variants (VGG16 and VGG19) which are pre-trained convolutional neural network (CNN) models. Experimental results showed that the CNN-based pre-trained techniques outperformed the conventional support vector machine (SVM)-based classifier in terms of classification accuracy. Proposed datasets have attained a low f-score when compared to existing datasets indicating they are very challenging.
Journal Article