Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Sk Aziz Ali
Rashed Al Koutayni
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Michael Fürst
Christiano Couto Gava
Tewodros Amberbir Habtegebrial
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Mina Ameli
Nareg Minaskan Karabid
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
René Schuster
Mohamed Selim
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Murad Almadani
Ahmet Firintepe
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
IEEE Winter Conference on Applications of Computer Vision (WACV-18), March 12-14, Lake Tahoe, NV/CA, USA
- Abstract:
- While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.