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Learning from 3D and Unstructured Data

Learning representations and modeling relations in a 3D context

Here you can find an overview of current research projects within this team. More details and related publications can be found on the respective project pages. Contact information for the team leader can be found down below.

Current Research Projects

Facial Paresis Analysis Teaser

Facial Paresis Analysis: Bridging the Gap - Mimics and Muscles

All facial motions for individual mimics and emotional expressions are possible using 43 different muscles controlled respectively by one facial nerve on both halves of the face. However, those nerves can be injured by external or internal factors, which in turn can cause a facial palsy. Medical experts evaluate this typically in a subjective manner. We want to measure it more objectively by exploiting machine learning methods. In the current project we aim to model the relationship between surface and underlying muscles using 3D sensors.

Time frame: 2019 - 2022

3D Semantic Segmentation Teaser

Semantic 3D Point Cloud Analysis of Outdoor Scenes

For LiDAR (Light detection and ranging) pulsed beams of light are used to measure distances from a scanner to the surface of objects in a scene to produce 3D point clouds. It is unstructured data composed of a collection of non-uniformly distributed points in a continuous space. In some cases, images are captured simultaneously during LiDAR campaigns to enrich these points with color information. We aim to assign one label from a set of pre-defined classes to each point of such a point cloud.

Time frame: 2017 - 2021

4D PAD Teaser

4D Presentation Attack Detection

The digitalization of organizational processes is progressing fast. At the same time, the need for robust, unsupervised authentication methods is increasing. Typical organizational processes are automated border control, the opening of a bank account, financial transfer, and mobile payments using self-service eGates, kiosks, and smart devices. However, current authentication methods are susceptible to advanced spoofing attacks or identity thefts. In this project we aim to develop more robust representations and methods based on temporal 3D sensor data.

Time frame: 2017 - 2020

Contact

Sven Sickert

Sven Sickert
Dr. rer. nat.


Room: 1211

Phone: (+49) 3641 9 46424