DFKI Augmented Vision recently released the first publicly available UWB Radar Driving Activity Dataset (RaDA), consisting of over 10k data samples from 10 different participants annotated with 6 driving activities. The dataset was recorded in the DFKI driving simulator environment. For more information and to download the dataset please check the project website:  https://projects.dfki.uni-kl.de/rada/

The dataset release is accompanied by an article publication at the Sensors journal:

Brishtel, Iuliia, Stephan Krauss, Mahdi Chamseddine, Jason Raphael Rambach, and Didier Stricker. “Driving Activity Recognition Using UWB Radar and Deep Neural Networks.” Sensors 23, no. 2 (2023): 818.

Contacts: Dr. Jason Rambach, Iuliia Brishtel

The Augmented Vision department of DFKI participated in the VIZTA project, coordinated by ST Microelectronics, aiming at developing innovative technologies in the field of optical sensors and laser sources for short to long-range 3D-imaging and to demonstrate their value in several key applications including automotive, security, smart buildings, mobile robotics for smart cities, and industry4.0.

The final project review was successfully completed in Grenoble, France on November 17th-18th, 2022. The schedule included presentations on the achievements of all partners as well as live demonstrators of the developed technologies. DFKI presented their smart building person detection demonstrator based on a top-down view from a Time-of-flight (ToF) camera, developed in cooperation with the project partner IEE. A second demonstrator, showing an in-cabin monitoring system based on a wide-field-of-view, which is installed in DFKIs lab has been presented in a video.

During VIZTA, several key results were obtained at DFKI on the topics of in-car and smart building monitoring including:

• 7 peer reviewed publications in conferences and journals
• 2 publicly available datasets (with over 140 downloads so far) available at  https://vizta-tof.kl.dfki.de/
• A live demonstrator of person detection  https://www.youtube.com/watch?v=ItCj0K99hlI

https://www.linkedin.com/company/vizta-ecsel-project/

Contact: Dr. Jason Rambach, Dr. Bruno Mirbach

DFKI Augmented Vision researchers Yongzhi Su, Praveen Nathan and Jason Rambach received their 1^st place award in the prestigious BOP Challenge 2022 in the categories Overall Best Segmentation Method and The Best BlenderProc-Trained Segmentation Method.

The BOP benchmark and challenge addresses the problem of 6-degree-of-freedom object pose estimation, which is of great importance for many applications such as robot grasping or augmented reality. This year, the BOP challenge was held within the “Recovering 6D Object Pose” Workshop at the European Conference on Computer Vision (ECCV) in Tel Aviv, Israel https://eccv2022.ecva.net/ . A total award of $4000 was distributed among the winning teams of the BOP challenge, donated by Meta Reality Labs and Niantic.

The awards were received by Dr. Jason Rambach on behalf of the DFKI Team and a short presentation of the method followed. The winning method was based on the CVPR 2022 paper “ZebraPose”  

ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Raphael Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari

The winning approach was develop by a team led by DFKI AV, with contributing researchers from TU Munich and Zhejiang University.

Contact: Yongzhi Su, Dr. Jason Rambach

Our Augmented Vision department is the coordinator of the new large European project “HumanTech”. The Kick-Off meeting was held on July 20th, 2022, at DFKI in Kaiserslautern. Please read the whole article here: Artificial intelligence for a safe and sustainable construction industry (dfki.de)

Please check out the article “Artificial intelligence for a safe and sustainable construction industry (dfki.de)” concerning the new EU project HumanTech which is coordinated by Dr. Jason Rambach, head of the Spatial Sensing and Machine Perception team (Augmented Reality/Augmented Vision department, Prof. Didier Stricker) at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern.

DFKI Augmented Vision had a strong presence in the recent CVPR 2022 Conference held on June 19th-23rd, 2022, in New Orleans, USA. The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event internationally. Homepage: https://cvpr2022.thecvf.com/ .

Overall, three publications were presented:

1. ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Raphael Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari

https://openaccess.thecvf.com/content/CVPR2022/papers/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.pdf

2. SOMSI: Spherical Novel View Synthesis with Soft Occlusion Multi-Sphere Images Tewodros A Habtegebrial, Christiano Gava, Marcel Rogge, Didier Stricker, Varun Jampani

https://openaccess.thecvf.com/content/CVPR2022/papers/Habtegebrial_SOMSI_Spherical_Novel_View_Synthesis_With_Soft_Occlusion_Multi-Sphere_Images_CVPR_2022_paper.pdf

3. Unsupervised Anomaly Detection from Time-of-Flight Depth Images
Pascal Schneider, Jason Rambach, Bruno Mirbach , Didier Stricker

https://openaccess.thecvf.com/content/CVPR2022W/PBVS/papers/Schneider_Unsupervised_Anomaly_Detection_From_Time-of-Flight_Depth_Images_CVPRW_2022_paper.pdf

On June 14th, 2022, Dr. Jason Rambach gave a keynote talk in the Computer Vision session of the  Franco-German Research and Innovation Network event held at the Inria headquarters in Versailles, Paris, France. In the talk, an overview of the current activities of the Spatial Sensing and Machine Perception team at DFKI Augmented Vision was presented.

On March 18th, 2022, René Schuster successfully defended his dissertation entitled “Data-driven and Sparse-to-Dense Concepts in Scene Flow Estimation for Automotive Applications”. The reviewers were Prof. Dr. Didier Stricker (Technical University of Kaiserslautern) and Prof. Dr. Andrés Bruhn (University of Stuttgart). Mr. Schuster received his doctorate from the Department of Computer Science at the Technical University of Kaiserslautern.

In his thesis, Mr. Schuster worked on three-dimensional motion estimation of the dynamic environment of vehicles. The focus was on machine learning methods, and the interpolation of individual estimates into a dense motion field. A particular challenge was the scarcity of annotated data for this problem and use case.

René Schuster received an M. Sc. in computational engineering from Darmstadt University of Technology in 2017. He then moved to DFKI to join the augmented reality group of Prof. Stricker. Much of his research was done in collaborative projects with BMW.

We are happy to announce that the Augmented Vision group will present two papers in the upcoming CVPR 2022 Conference from June 19th-23rd in New Orleans, USA. The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) is the premier annual computer vision event internationally. Homepage: https://cvpr2022.thecvf.com/

The two accepted papers are:

• ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
  Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Raphael Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari
  
  

Summary: ZebraPose sets a new paradigm on model-based 6DoF object pose estimation by using a binary object surface encoding to train a neural network to predict the locations of model vertices in a coarse to fine manner. ZebraPose shows a major improvement over the state-of-the-art on several datasets of the BOP Object Pose Estimation benchmark.

Preprint:  https://av.dfki.de/publications/zebrapose-coarse-to-fine-surface-encoding-for-6dof-object-pose-estimation/

Contact: Yongzhi Su, Dr. Jason Rambach

• SOMSI: Spherical Novel View Synthesis with Soft Occlusion Multi-Sphere Images
  Tewodros A Habtegebrial, Christiano Gava, Marcel Rogge, Didier Stricker, Varun Jampani

Summary: We propose a novel Multi-Sphere Image representation called Soft Occlusion MSI (SOMSI) and efficient rendering technique that produces accurate spherical novel-views from a sparse spherical light-field. SOMSI models appearance features in a smaller set (e.g. 3) of occlusion levels instead of larger number (e.g. 64) of MSI spheres. Experiments on both synthetic and real-world spherical light-fields demonstrate that using SOMSI can provide a good balance between accuracy and run-time. SOMSI view synthesis quality is on-par with state-of-the-art models like NeRF, while being 2 orders of magnitude faster.

For more information, please visit the project page at https://tedyhabtegebrial.github.io/somsi

Contact: Tewodros A Habtegebrial

We are happy to announce that our project DECODE has been accepted for the Nvidia Academic Hardware Grant. Nvidia will support our research in the field of human motion estimation and semantic reconstruction by donating a Nvidia A100 GPU for data centers. We will use the new hardware to accelerate our experiments for continual learning.