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
IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction
IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction
ernational Conference on Computer Vision and Pattern Recognition (CVPR-2019) Photogrammetric Computer Vision Workshop June 16-20 Long Beach CA United States 2019 .
- Abstract:
- The majority of the existing methods for non-rigid 3D surface regression from monocular 2D images require an object template or point tracks over multiple frames as an input, and are still far from real-time processing rates. In this work, we present the Isometry-Aware Monocular Generative Adversarial Network (IsMo-GAN) — an approach for direct 3D reconstruction from a single image, trained for the deformation model in an adversarial manner on a light-weight synthetic dataset. IsMo-GAN reconstructs surfaces from real images under varying illumination, camera poses, textures and shading at over 250 Hz. In multiple experiments, it consistently outperforms several approaches in the reconstruction accuracy, runtime, generalisation to unknown surfaces and robustness to occlusions. In comparison to the state-of-the-art, we reduce the reconstruction error by 10-30% including the textureless case and our surfaces evince fewer artefacts qualitatively.