Three papers accepted at ICPR 2020

We are proud to announce that the Augmented Vision group will present three papers in the upcoming ICPR 2020 conference which will take place from January 10th till 15th, 2021. The International Conference on Pattern Recognition (ICPR) is the premier world conference in Pattern Recognition. It covers both theoretical issues and applications of the discipline. The 25th event in this series is organized as an online virtual conference with more than 1800 participants expected.

The three accepted papers are:

1.  HPERL: 3D Human Pose Estimation from RGB and LiDAR
David Michael Fürst, Shriya T. P. Gupta, René Schuster, Oliver Wasenmüller, Didier Stricker
One sentence summary: HPERL proposes a two-stage 3D human pose detector that fuses RGB and LiDAR information for a precise localization in 3D.
Presentation date: PS T3.3, January 12th, 5 pm CET. 

2. ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching
Rishav, René Schuster, Ramy Battrawy, Oliver Wasenmüller, Didier Stricker
One sentence summary: ResFPN extends Feature Pyramid Networks by adding residual connections from higher resolution features maps to obtain stronger and better localized features for dense matching with deep neural networks.
This paper is accepted as an oral presentation (best 6% of all submissions).
Presentation date: OS T5.1, January 12th, 2 pm CET; PS T5.1, January 12th, 5 pm CET.

3. Ghost Target Detection in 3D Radar Data using Point Cloud based Deep Neural Network
Mahdi Chamseddine, Jason Rambach, Oliver Wasenmüller, Didier Stricker
One sentence summary: An extension to PointNet is developed and trained to detect ghost targets in 3D radar point clouds using labels by an automatic labelling algorithm.
Presentation date: PS T1.16, January 15th, 4:30 pm CET.

Four papers accepted at WACV 2021

The Winter Conference on Applications of Computer Vision (WACV 2021) is IEEE’s and the PAMI-TC’s premier meeting on applications of computer vision. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers. In 2021, the conference is organized as a virtual online event from January 5th till 9th, 2021.

The four accepted papers are:

1. SSGP: Sparse Spatial Guided Propagation for Robust and Generic Interpolation
René Schuster, Oliver Wasenmüller, Christian Unger, Didier Stricker
Q/A Session: Oral 1B, January 6th, 7 pm CET.

2. A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions
René Schuster, Christian Unger, Didier Stricker
Q/A Session: Oral 1C, January 6th, 7 pm CET.

3. SLAM in the Field: An Evaluation of Monocular Mapping and Localization on Challenging Dynamic Agricultural Environment
Fangwen Shu, Paul Lesur, Yaxu Xie, Alain Pagani, Didier Stricker

Abstract: This paper demonstrates a system capable of combining a sparse, indirect, monocular visual SLAM, with both offline and real-time Multi-View Stereo (MVS) reconstruction algorithms. This combination overcomes many obstacles encountered by autonomous vehicles or robots employed in agricultural environments, such as overly repetitive patterns, need for very detailed reconstructions, and abrupt movements caused by uneven roads. Furthermore, the use of a monocular SLAM makes our system much easier to integrate with an existing device, as we do not rely on a LiDAR (which is expensive and power consuming), or stereo camera (whose calibration is sensitive to external perturbation e.g. camera being displaced). To the best of our knowledge, this paper presents the first evaluation results for monocular SLAM, and our work further explores unsupervised depth estimation on this specific application scenario by simulating RGB-D SLAM to tackle the scale ambiguity, and shows our approach produces econstructions that are helpful to various agricultural tasks. Moreover, we highlight that our experiments provide meaningful insight to improve monocular SLAM systems under agricultural settings.

4. Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter, Didier Stricker

Abstract: Images recorded during the lifetime of computer vision based systems undergo a wide range of illumination and environmental conditions affecting the reliability of previously trained machine learning models. Image normalization is hence a valuable preprocessing component to enhance the models’ robustness. To this end, we introduce a new strategy for the cost function formulation of encoder-decoder networks to average out all the unimportant information in the input images (e.g. environmental features and illumination changes) to focus on the reconstruction of the salient features (e.g. class instances). Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety. Its applicability is assessed on three publicly available datasets. We combine the triplet loss as a regularizer in the latent space representation and a nearest neighbour search to improve the generalization to unseen illuminations and class instances. The importance of the aforementioned post-processing is highlighted on an automotive application. To this end, we release a synthetic dataset of sceneries from three different passenger compartments where each scenery is rendered under ten different illumination and environmental conditions:

Image belongs to paper no. 4.

Two new PhDs in November

Jameel Malik successfully defended his PhD thesis entitled “Deep Learning-based 3D Hand Pose and Shape Estimation from a Single Depth Image: Methods, Datasets and Application” in the presence of the PhD committee made up of Prof. Dr. Didier Stricker (Technische Universitat Kaiserslautern), Prof. Dr. Karsten Berns (Technische Universitat Kaiserslautern), Prof. Dr. Antonis Argyros (University of Crete) and Prof. Dr. Sebastian Michel (Technische Universitat Kaiserslautern) on Wednesday, November 11th, 2020.

In his thesis, Jameel Malik addressed the unique challenges of 3D hand pose and shape estimation, and proposed several deep learning based methods that achieve the state-of-the-art accuracy on public benchmarks. His work focuses on developing an effective interlink between the hand pose and shape using deep neural networks. This interlink allows to improve the accuracy of both estimates. His recent paper on 3D convolution based hand pose and shape estimation network was accepted at the premier conference IEEE/CVF CVPR 2020.

Jameel Malik recieved his bachelors and master degrees in electrical engineering from University of Engineering and Technology (UET) and National University of Sciences and Technology (NUST) Pakistan, respectively. Since 2017, he has been working at the Augmented Vision (AV) group DFKI as a researcher. His research interests include computer vision and deep learning. 

Mr. Malik right after his successful PhD defense.

A week later, on Thurday, November 19th, 2020, Mr. Markus Miezal also successfully defended his PhD thesis entitled “Models, methods and error source investigation for real-time Kalman filter based inertial human body tracking” in front of the PhD committee consisting of Prof. Dr. Didier Stricker (TU Kaiserslautern and DFKI), Prof. Dr. Björn Eskofier (FAU Erlangen) and Prof. Dr. Karsten Berns (TU Kaiserslautern).

The goal of the thesis is to work towards a robust human body tracking system based on inertial sensors. In particular the identification and impact of different error sources on tracking quality are investigated. Finally, the thesis proposes a real-time, magnetometer-free approach for tracking the lower body with ground contact and translation information. Among the first author publications of the contributions, one can find a journal article in MDPI Sensors and a conference paper on the ICRA 2017.

In 2010, Markus Miezal received his diploma in computer science from the University of Bremen, Germany and started working at the Augmented Vision group at DFKI on visual-inertial sensor fusion and body tracking. In 2015, he followed Dr. Gabriele Bleser into the newly founded interdisciplinary research group wearHEALTH at the TU Kaiserslautern, where the research on body tracking continued, focussing on health related applications such as gait analysis. While finishing his PhD thesis, he co-founded the company sci-track GmbH as spin-off from TU KL and DFKI GmbH, which aims to transfer robust inertial human body tracking algorithms as middleware to industry partners. In the future Markus will continue research at university and support the company.

Mr. Miezal celebrating the completion of his PhD.