Render three-dimensional data and maps with ease. Written as a self-study workbook, Introduction to 3D Data demystifies the sometimes confusing controls and procedures required for 3D modeling using software packages such as ArcGIS 3D Analyst and Google Earth. Going beyond the manual that comes with the software, this profusely illustrated guide explains how to use ESRI’s ArcGIS 3D Analyst to model and analyze three-dimensional geographical surfaces, create 3D data, and produce displays ranging from topographically realistic maps to 3D scenes and spherical earth-like views. The engagingly user-friendly instruction: • Walks you through basic concepts of 3D data, progressing to more advanced techniques such as calculating surface area and volume • Introduces you to two major software packages: ArcGIS 3D Analyst (including ArcScene and ArcGlobe) and Google Earth • Reinforces your understanding through in-depth discussions with over thirty hands-on exercises and tutorial datasets on the support website at www.wiley/college/kennedy • Helps you apply the theory with real-world applications Whether you’re a student or professional in geology, landscape architecture, transportation system planning, hydrology, or a related field, Introduction to 3D Data will quickly turn you into a power user of 3D GIS.
This updated bestseller provides an introduction to programming interactive computer graphics, with an emphasis on game development using DirectX 11. The book is divided into three main parts: basic mathematical tools, fundamental tasks in Direct3D, and techniques and special effects. It includes new Direct3D 11 features such as hardware tessellation, the compute shader, dynamic shader linkage and covers advanced rendering techniques such as screen-space ambient occlusion, level-of-detail handling, cascading shadow maps, volume rendering, and character animation. Includes a companion CD-ROM with code and figures. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected].
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.
3D deep learning is a rapidly evolving field that has the potential to transform various industries. This book provides a comprehensive overview of the current state-of-the-art in 3D deep learning, covering a wide range of research topics and applications. It collates the most recent research advances in 3D deep learning, including algorithms and applications, with a focus on efficient methods to tackle the key technical challenges in current 3D deep learning research and adoption, therefore making 3D deep learning more practical and feasible for real-world applications.This book is organized into five sections, each of which addresses different aspects of 3D deep learning. Section I: Sample Efficient 3D Deep Learning, focuses on developing efficient algorithms to build accurate 3D models with limited annotated samples. Section II: Representation Efficient 3D Deep Learning, deals with the challenge of developing efficient representations for dynamic 3D scenes and multiple 3D modalities. Section III: Robust 3D Deep Learning, presents methods for improving the robustness and reliability of deep learning models in real-world applications. Section IV: Resource Efficient 3D Deep Learning, explores ways to reduce the computation cost of 3D models and improve their efficiency in resource-limited environments. Section V: Emerging 3D Deep Learning Applications, showcases how 3D deep learning is transforming industries and enabling new applications for healthcare and manufacturing.This collection is a valuable resource for researchers and practitioners interested in exploring the potential of 3D deep learning.
This is the first book written on using Blender (an open-source visualization suite widely used in the entertainment and gaming industries) for scientific visualization. It is a practical and interesting introduction to Blender for understanding key parts of 3D rendering that pertain to the sciences via step-by-step guided tutorials. Any time you see an awesome science animation in the news, you will now know how to develop exciting visualizations and animations with your own data. 3D Scientific Visualization with Blender takes you through an understanding of 3D graphics and modeling for different visualization scenarios in the physical sciences. This includes guides and tutorials for: understanding and manipulating the interface; generating 3D models; understanding lighting, animation, and camera control; and scripting data import with the Python API. The agility of Blender and its well organized Python API make it an exciting and unique visualization suite every modern scientific/engineering workbench should include. Blender provides multiple scientific visualizations including: solid models/surfaces/rigid body simulations; data cubes/transparent/translucent rendering; 3D catalogs; N-body simulations; soft body simulations; surface/terrain maps; and phenomenological models. The possibilities for generating visualizations are considerable via this ever growing software package replete with a vast community of users providing support and ideas.
Introduction to 3D Game Programming with DirectX 9.0c: A Shader Approach presents an introduction to programming interactive computer graphics, with an emphasis on game development, using real-time shaders with DirectX 9.0. The book is divided into three parts that explain basic mathematical and 3D concepts, show how to describe 3D worlds and implement fundamental 3D rendering techniques, and demonstrate the application of Direct3D to create a variety of special effects. With this book understand basic mathematical tools used in video game creation such as vectors, matrices, and transformations; discover how to describe and draw interactive 3D scenes using Direct3D and the D3DX library; learn how to implement lighting, texture mapping, alpha blending, and stenciling using shaders and the high-level shading language (HLSL); explore a variety of techniques for creating special effects, including vertex blending, character animation, terrain rendering, multi-texturing, particle systems, reflections, shadows, and normal mapping;f ind out how to work with meshes, load and render .X files, program terrain/camera collision detection, and implement 3D object picking; review key ideas, gain programming experience, and explore new topics with the end-of-chapter exercises.
Introduction to Three-Dimensional Design is the first book to teach graphic design students the fundamentals of three-dimensional design through hands-on drawing and model projects. The book combines key concepts with carefully crafted exercises so students can apply three-dimensional design principles in practice. From initial sketches through experimental prototypes to the final model solutions, students will develop a deeper understanding of the often complex elements and principles of three-dimensional design.
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story
This textbook, first published in 2003, emphasises the fundamentals and the mathematics underlying computer graphics. The minimal prerequisites, a basic knowledge of calculus and vectors plus some programming experience in C or C++, make the book suitable for self study or for use as an advanced undergraduate or introductory graduate text. The author gives a thorough treatment of transformations and viewing, lighting and shading models, interpolation and averaging, Bézier curves and B-splines, ray tracing and radiosity, and intersection testing with rays. Additional topics, covered in less depth, include texture mapping and colour theory. The book covers some aspects of animation, including quaternions, orientation, and inverse kinematics, and includes source code for a Ray Tracing software package. The book is intended for use along with any OpenGL programming book, but the crucial features of OpenGL are briefly covered to help readers get up to speed. Accompanying software is available freely from the book's web site.
