Overview
- Open-access codes allow the reader to reproduce and reuse the results presented
- Provides public datasets for reproducible performance testing
- Shows the reader how to optimize 3-D Reconstructions
Part of the book series: SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC)
Part of the book sub series: SpringerBriefs in Signal Processing (BRIEFSSIGNAL)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods.
The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data.The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.
Authors and Affiliations
About the authors
Dr. YangQuan Chen is now a full professor at the School of Engineering, University of California, Merced. He was on faculty of Electrical and Computer Engineering Dept. of Utah State University, Logan, Utah from 2000 to 2012 where he was a tenured associate professor and the Director for the Center for Self-Organizing and Intelligent Systems (CSOIS) from 2004-2012. Dr. Chen published many books, papers and patents. He served as theGeneral Co-Chair for ICUAS 2014 and 2017, 2019 (International Conference on Unmanned Aircraft Systems), and was on the editorial board for journals like IEEE Transaction on Control Systems Technology, ISA Transactions, IFAC journals of Control Engineering Practice, ASME Journal of Dynamics Systems, Measurement and Control. He serves as associate editor for IFAC Mechatronics, Fractional Calculus and Applied Analysis, and Nonlinear Dynamics, as well as again ASME Journal of Dynamics Systems, Measurement and Control. He is now serving as the Topical Editor-in-Chief (Field Robotics) on International Journal of Advanced Robotics Systems (IJARS) and Intelligent Service Robotics. His Publon H-index is 63 with more than 17.1k citations, GoogleScholar citation is 39.7k with H-index 88, H10-index 512. Prof. Chen was selected in the 2018, 2019, 2020 Highly Cited Researchers List by Clarivate.
Bibliographic Information
Book Title: Towards Optimal Point Cloud Processing for 3D Reconstruction
Authors: Guoxiang Zhang, YangQuan Chen
Series Title: SpringerBriefs in Electrical and Computer Engineering
DOI: https://doi.org/10.1007/978-3-030-96110-7
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Softcover ISBN: 978-3-030-96109-1Published: 04 June 2022
eBook ISBN: 978-3-030-96110-7Published: 03 June 2022
Series ISSN: 2191-8112
Series E-ISSN: 2191-8120
Edition Number: 1
Number of Pages: XIX, 87
Number of Illustrations: 4 b/w illustrations, 25 illustrations in colour
Topics: Signal, Image and Speech Processing, Machine Learning, Robotics and Automation, Geotechnical Engineering & Applied Earth Sciences, Manufacturing, Machines, Tools, Processes