Research

My research interest lies at the intersection of computer vision and machine learning in the context of mobile perception systems. Currently, I am working on deep learning methods for 3D scene understanding to enable map-less driving or self-localization in sparse planning maps. In particular, I am interested in exploring how to overcome data shortage by leveraging the 6D pose awareness of mobile systems in highly accurate semantic 3D maps . Exampels of my current research are:

• Map Learning: Automatic data generation for deep learning applications using HD maps and multi-drive mapping
• Leveraging Map Learning for self-localisation, semantic mapping and map-less driving
• Learning-based 3D scene understanding

Teaching

• Übungsleiter der Vorlesung Messtechnik II (SS20, WS20/21)
• Aufgabensteller Grundlagen der Mess- und Regelungstechnik (SS19, WS19/20, SS20)
• Mechatronik-Praktikum (WS19/20)
• Rechnergestützte Verfahren in der Mess- und Regelungstechnik (WS19/20, WS20/21)

Academic Services

• Reviewer for
  • IEEE ITS Intelligent Vehicles Symposium (IV)
  • IEEE RAS International Conference for Robotics and Automatisation (ICRA)
  • IEEE RSJ International Conference on Intelligent Robots and Systems (IROS)
  • IEEE RAS Robotics and Automation Letters (RA-L)
  • De Gruyter TM-Technisches Messen (TM)
• Chair of special session on Real-time critical perception tasks in the context of automated driving at the FUSION 2021

Completed Theses

• Occlusion Handling for Automatic Data Generation using HD Maps and a highly accurate SLAM, master thesis, November 2020
• Fusion of Simultaneously Learned Semantic Information from Different Representations,  master thesis, November 2020
• A Comparison of Different Approachesto Solve the SLAM Problem on a Formula Student Driverless Race Car, master thesis, November 2020
• Combining Sequential LiDAR Measurements For Semantic Segmentation of Multi-Layer Grid Maps, master thesis, November 2020
• Panoptic Segmentation of Urban Scenarios, master thesis, September 2020

Publications

Juncong Fei, Kunyu Peng, Philipp Heidenreich, Frank Bieder, Christoph Stiller. PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data. In IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan, July 2021. [ http ]

Frank Bieder, Sascha Wirges, Sven Richter, Christoph Stiller. Fusion of Sequential LiDAR Measurements for Semantic Segmentation of Multi-Layer Grid Maps. tm - Technisches Messen, De Gruyter, 88, July 2021.

Nick Le Large, Frank Bieder, Martin Lauer. Comparison of Different SLAM Approaches for a Driverless Race Car / Ein Vergleich von unterschiedlichen Ansätzen zur simultanen Lokalisierung und Kartierung unter Verwendung eines fahrerlosen Rennfahrzeuges. tm - Technisches Messen, De Gruyter, 88, April 2021.

Kunyu Peng, Juncong Fei, Kailun Yang, Alina Roitberg, Jiaming Zhang, Frank Bieder, Philipp Heidenreich, Christoph Stiller, Rainer Stiefelhagen. MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding. 2021. under review. [ http ]

Frank Bieder, Sascha Wirges, Johannes Janosovits, Sven Richter, Zheyuan Wang, Christoph Stiller. Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data. In Proc. IEEE Int. Conf. Intelligent Vehicles, Las Vegas, USA, June 2020.

Frank Bieder, Muti Ur Rehman, Christoph Stiller. Fusion of Sequential Information for Semantic Grid Map Estimation. In Forum Bildverarbeitung, Karlsruhe, Germany, Nov 2020.