Prof. Dr. Didier Stricker

Dr. Alain Pagani

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Gabriele Bleser

Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Bertram Taetz

Dr. Muhammad Zeshan Afzal

Mhd Rashed Al Koutayni

Jilliam Maria Diaz Barros

Mahdi Chamseddine

Steve Dias da Cruz

Ahmet Firintepe

Sophie Folawiyo

David Michael Fürst

Kamalveerkaur Garewal

Christiano Couto Gava

Leif Eric Goebel

Tewodros Amberbir Habtegebrial

Jigyasa Singh Katrolia

Andreas Kölsch

Onorina Kovalenko

Muhammad Jameel Nawaz Malik

Nareg Minaskan Karabid

Mohammad Minouei

Manthan Pancholi

Engr. Kumail Raza

Dr. Nadia Robertini

María Alejandra Sánchez Marín

Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Lukas Stefan Staecker

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão

NRSfM-Flow: Recovering Non-Rigid Scene Flow from Monocular Image Sequences

NRSfM-Flow: Recovering Non-Rigid Scene Flow from Monocular Image Sequences
Vladislav Golyanik, Aman Shankar Mathur, Didier Stricker
Proceedings of the British Machine Vision Conference 2016 British Machine Vision Conference (BMVC-16), September 19-22, York, United Kingdom

Abstract:: Scene flow recovery from monocular image sequences is an emerging field in computer vision. While existing Monocular Scene Flow (MSF) methods extend the classical optical flow formulation to estimate depths/disparities and 3D motion, we propose a framework based on Non-Rigid Structure from Motion (NRSfM) technique — NRSfM-Flow. Therefore, both problems are formulated in the continuous domain and relation between them is established. To cope with real data, we propose two preprocessing steps for image sequences — redundancy removal and translation resolution — which increase quality of reconstructions and speedup computations. In contrast to the existing MSF methods which can cope with non-rigid deformations, our solution makes no strong assumptions about a scene such as known camera motion or camera velocity constancy and can handle occlusions. NRSfM-Flow is qualitatively evaluated on challenging real-world data. Experiments provide evidence that the proposed approach achieves high accuracy and outperforms state of the art in terms of the ability to reconstruct MSF with less prior knowledge about a scene.

Keywords:: monocular scene flow, non-rigid structure from motion, optical flow, 3D reconstruction, redundancy removal, translation resolution

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