Dr. Muhammad Zeshan Afzal

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

Mhd Rashed Al Koutayni

Jilliam Maria Diaz Barros

Iuliia Brishtel

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

An Adversarial Training based Framework for Depth Domain Adaptation

An Adversarial Training based Framework for Depth Domain Adaptation
Jigyasa Singh Katrolia, Lars Krämer, Jason Raphael Rambach, Bruno Mirbach, Didier Stricker
Proceedings of the 16th VISAPP. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP-2021) 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications February 8-10 online Springer 2021 .

Abstract:: In absence of sufficient labeled training data, it is common practice to resort to synthetic data with readily available annotations. However, some performance gap still exists between deep learning models trained on synthetic versus on real data. Using adversarial training based generative models, it is possible to translate images from synthetic to real domain and train on them easily generalizable models for real-world datasets, but the efficiency of this method is limited in the presence of large domain shifts such as between synthetic and real depth images characterized by depth sensor and scene dependent artifacts in the image. In this paper, we present an adversarial training based framework for adapting depth images from synthetic to real domain. We use a cyclic loss together with an adversarial loss to bring the two domains of synthetic and real depth images closer by translating synthetic images to real domain, and demonstrate the usefulness of synthetic images modified this way for training deep neural networks that can perform well on real images. We demonstrate our method for the application of person detection and segmentation in real-depth images captured in a car for in-cabin person monitoring. We also show through experiments the effect of using target domain image sets captured using different types of depth sensors on this domain adaptation approach.

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