Seminar Media Technology

Selected topics in media technology. The focus is on current research topics and new technologies. The participants study recent publications, prepare a summary in the form of a scientific paper and present the topic to the audience.

Details at the chairs webpage

Supervised Terms

  • SS21
  • WS20/21
  • SS20
  • WS19/20
  • SS19

SS21: Augmented Reality and Virtual Reality

  1. AR/VR-based Human-Robot Interfaces
  2. VR and AR Interfaces for Robot Learning from Demonstration
  3. Intuitive Teleoperation using Augmented Reality
  4. Improving Teleoperated Driving Using an Augmented Reality Representation
  5. Deep Learning for 6D pose estimation and its applications in AR
  6. Deformable Object Tracking for AR
  7. Marker-based Augmented Reality
  8. Visual Place Recognition
  9. Activity Recognition for Augmented Reality and Virtual Reality
  10. Augmented and Virtual Haptics
  11. Generation of Realistic Virtual Views
  12. Video coding optimization of virtual reality 360-degree Video

WS20/21: Recent Advances in Multimedia Compression - From 3D Point Clouds to Deep Neural Networks

  1. Compression Schemes for 360-degree Video Streaming
  2. Compression Artifacts Reduction for Compressed Image/Videos
  3. End-to-End Image/Video Compression With Neural Networks
  4. Semantic Image/Video Compression Through Deep Learning
  5. Video Compression for Low Delay Video Streaming
  6. Point Cloud Compression for Autonomous Driving
  7. Map Compression for Visual SLAM Systems
  8. Deep Autoencoder Model for Dynamically Acquired Point Cloud Data Compression Enhancement Using IMU Sensors
  9. Compression of Pose Graphs for SLAM systems
  10. Vibrotactile Perception and Compression
  11. Neural Network Model Compression
  12. Knowledge Distillation for Enhanced Neural Networks

SS20: Recent Advances in Multimedia Compression - From 3D Point Clouds to Deep Neural Networks

  1. Compression Schemes for 360-degree Video Streaming
  2. Compression Artifacts Reduction for Compressed Image/Videos
  3. End-to-End Image/Video Compression With Neural Networks
  4. Semantic Image/Video Compression Through Deep Learning
  5. Video Compression for Low Delay Video Streaming
  6. Point Cloud Compression for Autonomous Driving
  7. Map Compression for Visual SLAM Systems
  8. Deep Autoencoder Model for Dynamically Acquired Point Cloud Data Compression Enhancement Using IMU Sensors
  9. Log Compression for Robotics
  10. Vibrotactile Perception and Compression
  11. Neural Network Model Compression
  12. Knowledge Distillation for Enhanced Neural Networks

WS19/20: Autonomous Driving Methods

  1. Teleoperation for Autonomous Driving Failures
  2. Safety Concepts for Teleoperated Driving
  3. Early Failure Prediction in Autonomous Driving
  4. Out-Of-Distribution Detection for Autonomous Driving
  5. Visual SLAM Methods
  6. Path Planning
  7. Driver intention/motion prediction
  8. Personalized Autonomous Driving: Learning from Driver Demonstrations
  9. Multi-modal Object Detection
  10. Predictive Methods for Network Delay Compensation by Teledriving
  11. Vision Enhancement for Autonomous Driving under Adverse Weather Conditions
  12. Self-supervised Learning of Depth Estimation and Ego-Motion from Monocular Videos using Convolutional Neural Networks

SS19: Machine Learning

  1. Human Visual Perception as a Model for Neural Networks
  2. Dimension Reduction - Extracting Information from High-Dimensional Data
  3. Embedded Deep Learning for Sensor Fusion Applications
  4. Throughput Prediction in Cellular Networks
  5. Region of Interest Prediction for Teleoperated Driving Applications
  6. Quality of Experience Prediction in Mobile Video
  7. Image Quality Assessment
  8. Image Restoration - from Conventional Methods to Neural Networks Approaches
  9. Content-Based Image Retrieval
  10. Machine Learning for Path Planning
  11. Neural SLAM
  12. Tactile Object Description and Exploration
Markus Hofbauer
Markus Hofbauer
Research Associate and Software Engineer

Research Associate at the Chair of Media Technology and Software Engineer.