January 2021

We are excited to announce that the Augmented Vision group will present 3 papers in the upcoming VISAPP 2021 Conference, February 8th-10th, 2021:

The International Conference on Computer Vision Theory and Applications (VISAPP) is part of VISIGRAPP, the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. VISAPP aims at becoming a major point of contact between researchers, engineers and practitioners on the area of computer vision application systems. Homepage: http://www.visapp.visigrapp.org/

The 3 accepted papers are:

1.  An Adversarial Training based Framework for Depth Domain Adaptation
Jigyasa Singh Katrolia, Lars Krämer, Jason Raphael Rambach, Bruno Mirbach, Didier Stricker
One sentence summary: The paper presents a GAN-based method for domain adaptation between depth images.

2. OFFSED: Off-Road Semantic Segmentation Dataset
Peter Neigel, Jason Raphael Rambach, Didier Stricker
One sentence summary: A dataset for semantic segmentation in off-road scenes for automotive applications is made publically available.

3. SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences
Dennis Stumpf, Stephan Krauß, Gerd Reis, Oliver Wasenmüller, Didier Stricker
One sentence summary: SALT proposes a simple and effective tool to facilitate the annotation process for segmentation and detection ground truth data in RGB-D video sequences.

We are happy to announce that our paper “SynPo-Net–Accurate and Fast CNN-Based 6DoF Object Pose Estimation Using Synthetic Training” has been accepted for publication at the MDPI Sensors journal, Special Issue Object Tracking and Motion Analysis. Sensors (ISSN 1424-8220; CODEN: SENSC9) is the leading international peer-reviewed open access journal on the science and technology of sensors.

Abstract: Estimation and tracking of 6DoF poses of objects in images is a challenging problem of great importance for robotic interaction and augmented reality. Recent approaches applying deep neural networks for pose estimation have shown encouraging results. However, most of them rely on training with real images of objects with severe limitations concerning ground truth pose acquisition, full coverage of possible poses, and training dataset scaling and generalization capability. This paper presents a novel approach using a Convolutional Neural Network (CNN) trained exclusively on single-channel Synthetic images of objects to regress 6DoF object Poses directly (SynPo-Net). The proposed SynPo-Net is a network architecture specifically designed for pose regression and a proposed domain adaptation scheme transforming real and synthetic images into an intermediate domain that is better fit for establishing correspondences. The extensive evaluation shows that our approach significantly outperforms the state-of-the-art using synthetic training in terms of both accuracy and speed. Our system can be used to estimate the 6DoF pose from a single frame, or be integrated into a tracking system to provide the initial pose.

Authors: Yongzhi Su, Jason Raphael Rambach, Alain Pagani, Didier Stricker

Article: https://av.dfki.de/publications/synpo-net-accurate-and-fast-cnn-based-6dof-object-pose-estimation-using-synthetic-training/

Contact: Yongzhi.Su@dfki.de, Jason.Rambach@dfki.de

After two years of collaborative work, the project ArInfuse is inviting for its final workshop on January 28th.

ARinfuse is an Erasmus+ project that aims to infuse skills in Augmented Reality for geospatial information management in the context of utility underground infrastructures, such as water, sewage, electricity, gas and fiber optics. In this field, there is a real need for an accurate positioning of the underground utilities, to avoid damages to the existing infrastructures. Information communication technologies (ICT), in fusion with global navigation satellite systems (GNSS), GIS and geodatabases and augmented/virtual reality (AR/VR) are able to offer the possibility to convert the geospatial information of the underground utilities into a powerful tool for field workers, engineers and managers.
ARinfuse is mainly addressed to technical professional profiles (future and current) in the utility sector that use, or are planning to use AR technology into practical applications of ordinary management and maintenance of utility networks.

The workshop entitled “Exploiting the potential of Augmented Reality & Geospatial Technologies within the utilities sector” is addressed to engineering students and professionals that are interested in the function, appliance and benefits of AR and geospatial technologies in the utilities sector.

The workshop will also introduce the ARinfuse catalogue of training modules on Augmented Reality and Geoinformatics applied within the utility infrastructure sector.

Registration: https://www.arinfuse.eu/arinfuse-online-workshop-register/
More information: https://www.arinfuse.eu/join-the-final-arinfuse-online-event-training-seminar-thursday-28-01-2021/

Contact persons: Dr. Alain Pagani and Narek Minaskan