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Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
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Alaa Alshubbak
Yuriy Anisimov
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Ramy Battrawy
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Mahdi Chamseddine
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Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
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Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
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Manthan Pancholi
Qinzhuan Qian
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Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
Consolidating Segmentwise Non-Rigid Structure from Motion
Consolidating Segmentwise Non-Rigid Structure from Motion
International Conference on Machine Vision Applications (MVA). IAPR Conference on Machine Vision Applications (MVA-2019) May 27-31 Tokyo Japan IAPR 2019 .
- Abstract:
- This paper introduces a new segmentwise technique which consolidates multiple principles for non-rigid structure from motion (NRSfM) into a single energy-based framework. The energy functional of our Consolidating Monocular Dynamic Reconstruction (CMDR) approach is optimised by non-linear least squares and includes terms allowing to define the deformation model and additional constraints simultaneously in the metric and trajectory spaces. The proposed method achieves high accuracy on several tested sequences while providing robustness and scalability due to the spatial scene segmentation and the new lifted spatial Laplacian term. CMDR is flexible and easy to implement, thanks to the unified optimisation framework. It allows for scenario-specific extensions and can be used for rapid prototyping of new NRSfM methods.