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Prof. Dr. Didier Stricker

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
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine
Steve Dias da Cruz

Fangwen Shu

Torben Fetzer

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
Paul Lesur

Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal

Mina Ameli

Nareg Minaskan Karabid
Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Qinzhuan Qian

Engr. Kumail Raza
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
A Framework for an Accurate Point Cloud Based Registration of Full 3D Human Body Scans
A Framework for an Accurate Point Cloud Based Registration of Full 3D Human Body Scans
Vladislav Golyanik, Gerd Reis, Bertram Taetz, Didier Stricker
IAPR Conference on Machine Vision Applications (MVA-17), March 8-12, Nagoya, Japan
- Abstract:
- Alignment of 3D human body scans is a challenging problem in computer vision with various applications. While being extensively studied for the mesh-based case, it is still involved if scans lack topology. In this paper, we propose a practical solution to the point cloud based registration of 3D human scans and a 3D human template. We adopt recent advances in point set registration with prior matches and design a fully automated registration framework. Our framework consists of several steps including establishment of prior matches, alignment of point clouds into a common reference frame, global non-rigid registration, partial non-rigid registration, and a post-processing step. We can handle large point clouds with significant variations in appearance automatically and achieve high registration accuracy which is shown experimentally. Finally, we demonstrate a pipeline for treatment of social pathologies with animatable virtual avatars as an exemplary real-world application of the new framework.