Introduction To 3d Data
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Introduction to 3D Data
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.
Introduction to 3D Data
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.
Deep Learning For 3d Vision: Algorithms And Applications
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.