Search
Publication Authors

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
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker
Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision (WACV-2021) January 5-9 Waikoloa HI United States IEEE 2021 .
- Abstract:
- Interpolation of sparse pixel information towards a dense target resolution finds its application across multiple disciplines in computer vision. State-of-the-art interpolation of motion fields applies model-based interpolation that makes use of edge information extracted from the target image. For depth completion, data-driven learning approaches are widespread. Our work is inspired by latest trends in depth completion that tackle the problem of dense guidance for sparse information. We extend these ideas and create a generic cross-domain architecture that can be applied for a multitude of interpolation problems like optical flow, scene flow, or depth completion. In our experiments, we show that our proposed concept of Sparse Spatial Guided Propagation (SSGP) achieves improvements to robustness, accuracy, or speed compared to specialized algorithms.