Katharina Wäschle


Fully automated 3D-data interpretation by deep learning

With the availability of a multitude of increasingly sensitive environmental sensors that produce streams of raw data, scaling the interpretation of said data is a pressing issue. Deep learning offers a set of tools to help make sense of huge amounts of data without requiring manual analysis of every data point. We describe a pipeline that produces a semantic interpretation of a georeferenced point cloud produced from 3D laser scanning with the help of complementary 2D RGB image data. A fully convolutional neural network trained on a large amount of custom collected and annotated data performs a semantic segmentation of the images; we then fuse segmented images and point cloud to extract 3D object instances, which serve as input for different applications.

Curriculum Vitae

Katharina Wäschle is a postdoctoral researcher at the Object and Shape Detection Department at Fraunhofer IPM. She received her PhD on integrating user feedback on machine translations via machine learning from Heidelberg University in 2015. Previously, she worked at Heidelberg University Publishing in software development and research data management. Her main research interests are data processing and analysis for (deep) machine learning.