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
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.