Capture, fusion and positioning, automated interpretation of 3D data
Fraunhofer IPM covers the complete process chain of 3D data acquisition: from robust laser scanners for the acquisition of 3D structures to the interpretation and visualization of 3D data.

Condition monitoring generates large amounts of data.#

Automated object recognition accelerates the evaluation process.

© Fraunhofer/Ines Escherich
"Joseph von Fraunhofer Prize 2019": Prof. Dr. Alexander Reiterer (2.f.l.), Dr. Katharina Wäschle (c.) and Dominik Störk (2.f.r.) from Fraunhofer IPM accept the research award from jury member Prof. Dr. Gerd Müller (l.) and Prof. Dr.-Ing. Reimund Neugebauer (r.), President of the Fraunhofer-Gesellschaft.

Fraunhofer Prize 2019#

A team from Fraunhofer IPM was awarded for the development of a fully automated data evaluation and object recognition. The software speeds up infrastructure planning considerably.

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Automated Data Interpretation

Automated interpretation of 3D and image data

Today, high-performance cameras or laser scanners are used for many surveying tasks and condition monitoring. They deliver high-resolution images and very accurate, geo-referenced measurement data. The data are usually interpreted manually. Advances in artificial intelligence enable the use of innovative techniques and algorithms to automate the evaluation process.

Today, complex learning algorithms based on the concept of »deep learning« with artificial neural networks (ANN) are used for the evaluation of 3D data. These algorithms have been shown to be superior to traditional methods of object recognition. Just a few years ago, the situation was different: The training of bespoke algorithms took weeks or even months. Today, thanks to massive parallelization, this process can be accomplished in just a few hours. The evaluation of new data sets based on a trained ANN is then even conducted in real time. In ANN, the fed-in information passes through a large number of interconnected artificial neurons, where it is processed and passed to other neurons. ANNs learn the output patterns corresponding to specific input patterns with the help of manually annotated training data. Based on this »experience«, new types of input data can then be analyzed in real time. ANNs have proven to be very robust to variations on characteristic colors, edges and shapes.


Transport Infrastucture


Objective and reliable analysis of condition monitoring data



Structural Features


Automatic recognition of surface distress in data

Further information#


Fraunhofer Initiative »Lighthouse Projects«

»Cognitive Agriculture«

Since November 2018, eight Fraunhofer Institutes have jointly been conducting research on technologies for the digitalization, automation, and electrification of agricultural processes. Fraunhofer IPM's task within this project is to develop robust 3D sensors, sensors for N2O based on laser spectroscopy, multispectral laser scanning methods and specific tools for data processing.



Building and construction surveying with laser scanning technology

Topics: Automated object regocnition in 3D data, data acquisition from the air, sensor concept for tunnel inspection.


Press release / 11.10.2018

Software supports optimized route planning

Deutsche Telekom AG will optimize the planning for new fiber-optic cables in the future using infrastructure data it collects itself. To achieve this, the company is investing in optical measurement technology with automated data analysis.