Overview
- Computer vision primer: state-of-the-art methods
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
- Non-Rigid Structure from Motion
- NRSfM
- Scalable Monocular Surface Reconstruction
- Shape Priors for Non-rigid Structure from Motion
- Monocular Surface Regression Networks
- Coherent Depth Fields
- High Dimensional Space Model
- NRSfM with the State Recurrence Constraint
- Probabilistic Point Set Registration with Prior Matches
- Extended Coherent Point Drift
- Human Appearance Transfer
- Gravitational Approach for Point Set Registration
- Barnes–Hut Rigid Gravitational Approach
- Monocular Scene Flow Estimation
- RGB-D Based Scene Flow Estimation
About this book
Vladislav Golyanik proposes several new methods for dense non-rigid structure from motion (NRSfM) as well as alignment of point clouds. The introduced methods improve the state of the art in various aspects, i.e. in the ability to handle inaccurate point tracks and 3D data with contaminations. NRSfM with shape priors obtained on-the-fly from several unoccluded frames of the sequence and the new gravitational class of methods for point set alignment represent the primary contributions of this book.
About the Author:
Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors forcomputer vision and graphics (e.g., quantum computers and event cameras).
Authors and Affiliations
About the author
Vladislav Golyanik is currently a postdoctoral researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. The current focus of his research lies on 3D reconstruction and analysis of general deformable scenes, 3D reconstruction of human body and matching problems on point sets and graphs. He is interested in machine learning (both supervised and unsupervised), physics-based methods as well as new hardware and sensors for computer vision and graphics (e.g., quantum computers and event cameras).
Bibliographic Information
Book Title: Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds
Authors: Vladislav Golyanik
DOI: https://doi.org/10.1007/978-3-658-30567-3
Publisher: Springer Vieweg Wiesbaden
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020
Softcover ISBN: 978-3-658-30566-6Published: 05 June 2020
eBook ISBN: 978-3-658-30567-3Published: 04 June 2020
Edition Number: 1
Number of Pages: XXIV, 352
Number of Illustrations: 106 b/w illustrations, 13 illustrations in colour
Topics: Image Processing and Computer Vision, Computer Imaging, Vision, Pattern Recognition and Graphics, Artificial Intelligence, Machine Learning