Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds

1. Auflage 2020
ISBN: 978-3-658-30566-6
Verlag: Springer

Buch, Englisch, 352 Seiten, Format (B × H): 148 mm x 210 mm, Gewicht: 486 g

Reihe: Research

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). 

Zielgruppe

Research

Autoren/Hrsg.

Golyanik, Vladislav

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision

Inhaltsverzeichnis

Scalable Dense Non-rigid Structure from Motion.- Shape Priors in Dense Non-rigid Structure from Motion.- Probabilistic Point Set Registration with Prior Correspondences.- Point Set Registration Relying on Principles of Particle Dynamics.

Über Autor(innen)

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).