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Reconstruction and Analysis of 3D Scenes: From Irregularly Distributed 3D Points to Object Classes de Martin Weinmann

Reconstruction and Analysis of 3D Scenes: From Irregularly Distributed 3D Points to Object Classes

Autor Martin Weinmann
en Limba Engleză Hardback – 31 mar 2016
This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.
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Specificații

ISBN-13: 9783319292441
ISBN-10: 3319292447
Pagini: 233
Ilustrații: XXII, 233 p. 81 illus., 69 illus. in color.
Dimensiuni: 155 x 235 x 20 mm
Greutate: 0.64 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Preliminaries of 3D Point Cloud Processing.- A Brief Survey on 2D and 3D Feature Extraction.- Point Cloud Registration.- Co-Registration of 2D Imagery and 3D Point Cloud Data.- 3D Scene Analysis.- Conclusions and Future Work.

Recenzii

“The book … is complete summary for understanding the reconstruction of 3D scenes through the use of point clouds. … The ‘book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the- art techniques.’ This book is highly useful to those unfamiliar with laser scanner data gathering including Lidar and would serve as a good first choice to educators teaching about these technologies and processing applications.” (Jeff Thurston, 3D Visualization World Magazine, 3dvisworld.com, October, 2016)

Textul de pe ultima copertă

This unique text/reference presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced for the complete processing workflow, incorporating the filtering of noisy data, the extraction of appropriate features, the alignment of 3D point clouds in a common coordinate frame, the enrichment of 3D point cloud data with other types of information, and the semantic interpretation of 3D point clouds. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques.
Topics and features:
  • Reviews techniques for the acquisition of 3D point cloud data and for point quality assessment
  • Explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data
  • Proposes an original approach to keypoint-based point cloud registration
  • Discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping
  • Presents a novel framework for 3D scene analysis, addressing neighborhood selection, feature extraction, feature selection, and classification
  • Covers each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene
This clearly-structured and accessible work will be of great interest to a broad audience, from students at undergraduate or graduate level, to lecturers, practitioners and researchers in photogrammetry, remote sensing, computer vision and robotics.

Caracteristici

Reviews the latest research on 3D point cloud generation, feature extraction, point cloud registration, feature selection and 3D scene analysis Introduces a new framework for advanced point cloud processing from raw data to semantic objects Includes detailed evaluations of methods on various benchmark datasets, in order to provide objective results and conclusions Includes supplementary material: sn.pub/extras