DYNAMICS

Contact person: Dr. Kiran Varanasi
Funding by: BMBF
Grant agreement no.: 01IW15003
Begin: 01.08.2015
End: 31.07.2018

The objective of DYNAMICS is to develop a new methodology for 4D reconstruction of real world scenes with a small number of cameras, as well as to learn statistical models from the captured data sets. A 4D reconstruction refers to a sequence of accurate 3D reconstructions (including geometry, topology and surface properties) of a dynamic (evolving in time) real-world scene. We aim to build a robust lightweight capture system that can be easily installed and used (e.g. in the living room of a house, in outdoor environments, and broadly under various spatial and temporal constraints).

We are developing a novel interactive software system for motion estimation capitalizing on our experience from the predecessor project DENSITY and exploring new directions (new hardware and machine learning methods).

Specifically, the project DYNAMICS can be subdivided into several work packages according to the target scenarios and concerned areas of computer vision:

1) Software for an interactive monocular 4D reconstruction of non-rigid scenes. The main components are modules dealing with non-rigid structure from motion
(NRSfM) pipeline and non-rigid registration. Underlying technology will allow to reconstruct non-rigidly deforming scenes with a minimal number of assumptions from a single RGB camera.
Target scenarios include endoscopy, capture of facial expressions, small motion and post-factum reconstructions.

2) Software for robust 4D reconstruction from multiple views incorporating optical flow and scene flow with additional assumptions. We plan to assemble a capture studio with five Emergent HT-4000C high-speed cameras (a multi-view setting). Here, we aim at the highest precision and richness of detail in the reconstructions.

3) 3D shape templates with attributes derived from real data using deep learning techniques. The main objective of this work package is to provide statistical models as a prior knowledge in order to increase the robustness and accuracy of reconstructions. Furthermore, the shape templates will allow for more accurate reconstructions of articulated motion (e.g. skeleton poses) from uncalibrated
multi-view settings.

DYNAMICS is a BMBF project with an emphasis on development of core technologies applicable in other ongoing and forthcoming projects in the Augmented Vision Lab.

ServiceFactory

Contact person: Manthan Pancholi
Funding by: BMWi
Grant agreement no.: 01MD16003F
Begin: 01.01.2016
End: 30.06.2018

Project Partners:

            The aim of this research project is to create an open platform and associated digital infrastructure in the form of interfaces and architectures that allow market participants to achieve simple, secure and fair participation at different levels of value creation. This platform will be designed for the recording, analysis and aggregation of data acquired from everyday used sensors (Smart Objects), as well as the conversion of these data into digital services (Smart Services), both technically and with regard to the underlying business and business model. Furthermore, for an initial technical product area, sports shoes will be considered, the possibility is exerted to extend these to cyberphysical systems which go beyond the existing possibilities of data collection (pure sensors). This is to create a broadly communicable demonstrator object that can bring the possibilities of smart services and intelligent digitization to the general public. In addition, the technical prerequisites as well as suitable business models and processes are created in order to collect the data under strict observance of the legal framework as well as the consumer and data protection for the optimization of product development, production and logistics chains. Here, too, an open structure will allow market participants to work with these data at different levels of added value.

During the project period, national standardization and work for the necessary internationalization can be started in parallel, and all preparatory work should be done by the end of the project. Standardization is the foundation for trust to motivate companies to participate in the platform. On the basis of the defined standards a certification can take place. This is necessary to ensure data exchange (encryption) and the security of the networks (against cybercrime). This also includes clarifying questions about the right to data in the cloud.

SwarmTrack

Contact person: Stephan Krauß
Funding by: Stiftung RLP Innovation

The goal of the SwarmTrack project is the research and development of a novel method for accurate automatic tracking of objects moving in groups.

A common problem in object tracking are occlusions, where moving objects or static parts of the scene obstruct the view on the target objects. This may lead to tracking loss or wrong assignment of identity labels. Another important issue is confusion where nearby objects have a similar or identical appearance. This may lead to errors in the identity assignment, where labels are switched between objects.

SwarmTrack investigates possibilities for exploiting the group structure in the spatial and temporal domain to derive clues and constraints for correct identity assignment after occlusions and in the presence of similar objects. Such clues include the spatial arrangement of the tracked objects and its changes over time as well as motion continuity and coherency constraints. Furthermore, SwarmTrack investigates methods for the automatic creation of coherent groups as well as updating them, when objects leave the group or new objects join.

The resulting multi-target tracking approach has applications in traffic monitoring and analysis as well as other fields where objects move coherently in groups.

Body Analyzer

Contact person: Oliver Wasenmüller
Funding by: BMBF
Grant agreement no.: 01IS12050
Begin: 01.03.2015
End:  31.05.2016

Reconstruction and analyze of 3D human body models

Individual anthropometric measurements build the basis for a wide range of applications such as custom clothing or biometric identity verification. The possibility to extract these data automatically from 3D body models is therefore from high importance. Within the ‘Body Analyzer’ project a body scanner has been developed that is not only precise but also robust and manageable.

Extracted anthropometric measurements for five exemplary human body scans (Wasenmüller et al., 3DBST 2015)

Body analyzer: precise, robust, manageable 

In this project, a body scanner has been developed, which is distinguished from its competition by its precision, robustness and manageability as well as the addition of an analysis component as a body analyzer. The required hardware for capturing the data is thereby reduced to a single camera.

 

Joint use of color and depth information

A visual body scanner reconstructs a closed 3D model of a person from different camera views. These views can be obtained on the basis of a camera by rotating the person in front of this camera around its own axis, or by moving the camera around the person. If a depth camera is used, recent filter and global non-rigid registration methods are used to transform the recorded data into a consistent body model.

Generic template point clouds (left) and overlay of the registered template (middle) with the human body scan (Wasenmüller et al., 3DBST 2015)

Such systems have already been developed and published in the past. However, these systems have some limitations in precision, detail, and robustness that have made practical use difficult to date.

In this project, the limitations were largely eliminated by the joint use of color and depth information, and the system was expanded to include a cost-effective, non-technical body analyzer by supplementing an analytical component for extracting anthropometric measurements. Therefore, the project proposes a new method to define anthropometric measures once on a generic template using landmarks. After the initial definition the template can be registered against an individual body scan and the landmarks can be transferred to the scan using a new proposed algorithm. Exemplary extracted measurements are displayed in the figure above.

 

Further detail regarding RGB-D reconstruction and non-rigid movements (Link) and Precise and Automatic Anthropometric Measurement Extraction using Template Registration (Link).

ARVIDA
arvida-logo

Contact person: Oliver Wasenmüller
Funding by: BMBF
Grant agreement no.: 01IM13001J
Begin: 01.09.2013
End: 31.10.2016

3D Discrepancy Check for virtual product verification

The department Augmented Vision at DFKI deals in this project with a 3D Discrepancy Check for virtual product verification. Thereby a real object is captured in real-time with a RGB-D camera and compared to a reference model (e.g. CAD) of the object. Though algorithms for high precision reconstruction of objects with small or medium size using depth cameras are investigated and developed.

Capture of objects in real-time with RGB-D cameras

Discrepancy check is a well-known task in industrial application. Within this project, the department Augmented Vision at DFKI presents a new approach for Augmented Reality 3D Discrepancy Check consisting of three main contributions. First, a new two-step depth mapping algorithm for RGB-D cameras is proposed, which fuses depth images into consistent 3D models with an accuracy of around 0.01m outperforming state-of-the-art algorithms. Second, a semi-automatic alignment algorithm is proposed, which rapidly aligns a reference model to the reconstruction and third, an algorithm for 3D discrepancy check based on pre-computed distances is proposed.

The results of the project were summarized and published in the following paper: 3D discrepancy check

The new AR discrepancy check, which is able to capture scene geometry in real-time using a RGB-D camera (Wasenmüller et al., IEEE ISMAR 2016)
The new AR discrepancy check, which is able to capture scene geometry in real-time using a RGB-D camera (Wasenmüller et al., IEEE ISMAR 2016)

ARVIDA – Service oriented reference architecture for virtual technologies (VT)

The research on Discrepancy Check was performed within the ARVIDA project. This project is funded by the German Federal Ministry for Education and Research (BMBF) with actually 24 partners from research and industry. The main goal of the project ARVIDA is the creation of a service oriented reference architecture for virtual technologies (VT). The service orientation and the usage or rather adaption of available internet and VT-standards ensure interoperability between different modules and VT applications. A broad cross-company evaluation of the reference architecture in selected industrial scenarios guarantees that the results can be used as a future standard. As one exemplary application the 3D Discrepancy Check was developed.

Eyes Of Things

Contact person: Dr.-Ing. Alain Pagani
Funding by: EU
Grant agreement no.: 643924
Funding programm: H2020
Begin: 01.01.2015
End: 30.06.2018

The aim of the European Eyes of Things it to build a generic Vision device for the Internet of Things.

The device will include a miniature camera and a specific Vision Processing Unit in order to perform all necessary processing tasks directly on the device without the need of transferring the entire images to a distant server. The envisioned applications will enable smart systems to perceive their environment longer and more interactively.

The technology will be demonstrated with applications such as Augmented Reality, Wearable Computing and Ambient Assisted Living.

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LARA
lara logo

Contact person: Dr.-Ing. Alain Pagani
Funding by: EU
Grant agreement no.: 641460

Funding programm: H2020

Begin: 01.02.2015
End: 30.06.2017

LARA is a European Project aiming at developing a new mobile device for helping employees of utilities companies in their work on the field. The device to be developed – called the LARA System – consists of a tactile tablet and a set of sensors that can geolocalise the device using the European GALILEO system and EGNOS capabilities.

The LARA system is produced under a collaborative work where different players, SMEs, large companies, universities and research institutes are contributing with different expertise.

LBS & Augmented Reality Assistive System for Utilities Infrastructure Management through Galileo and EGNOS

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EASY-IMP

Contact person: Dr.-Ing. Norbert Schmitz
Funding by: EU
Grant agreement no.: 609078
Funding programm: FP7
Begin: 01.09.2013
End: 31.08.2016

EASY-IMP is a large initiative started in September 2013. It regroups 12 partners from seven different countries to develop new methodologies for designing and producing intelligent wearable products as Meta-Products.

We propose a Cloud-enabled framework for Collaborative Design and Development of Personalised Products and Services, combining embedded  (Internet of things paradigm) and mobile devices with facilities for joint open development of enabling downloadable applications.

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AlterEgo

Contact person: Didier Stricker
Funding by: EU
Grant agreement no.: 600610
Funding programm: FP7
Begin: 01.02.2013
End: 31.07.2016

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Density

Contact person: Dr. Bertram Taetz
Funding by: BMBF
Grant agreement no.: 01IW12001
Begin: 01.07.2012
End: 30.06.2015

The goal of DENSITY is to develop a new methodology for 3D reconstruction suitable to inexperienced end-users. The basic idea relies on the observation that it is difficult to control the user environment and camera settings, and that certain knowledge is needed in order to take “appropriate” pictures.

Our easy-to-use, cost-effective scanning solution which is based on such a sensor could make 3D scanning technology more accessible to everyday users. Instead of guiding the user in trying to reduce the number of necessary pictures to a minimum, which leads in practice to very unreliable results, we propose to make use of depth images (RGB-D images) available from using low-cost depth cameras (Kinect or Time-of-Flight sensor). The depth images of those low cost depth cameras are currently of low resolution and noisy, but provide relatively stable results and a good coverage of the perceived area.

However, those partial and coarse 3D views still need to be registered and refined in order to be useful in our scanning task.

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