Organised by
ISIF
In Association with
USAL UC3M
Supported by
IEEE

SS17 - Probabilistic RGBD Data Fusion

In contrast to a single sensor,  the  combination of multiple cameras brings several advantages, including   simultaneous coverage of a large environment, increased resolution,   redundancy, and robustness against occlusion. However, together with  these great benefits a variety of challenges  arise: synchronization,  calibration, registration, multi-sensor fusion,  large amounts of data,  interference issues, and last but not least,  sensor-specific stochastic  and set-valued uncertainties. 

This  Special Session addresses fundamental techniques, recent  developments  and future research directions in the field of  probabilistic RGBD data  fusion. 

Topics of interest 

  •  Methodologies for probabilistic RGBD data fusion: Bayesian inference, nonlinear filtering, random sets, data association 
  •  Sensor models 
  •  Sensor management, calibration, registration, synchronization 
  •  Sensors: Radar devices, laser rangefinders, RGB cameras, depth cameras 
  •  Applications: Surveillance, telepresence, motion capturing, 3D reconstruction, robotics, medicine, biology, computer vision 
  •  Case studies: Benchmark scenarios, performance measures 

Keywords 

RGBD sensor (networks), point cloud fusion, object tracking, calibration 

Special Session Organizers

  • Uwe D. Hanebeck, Karlsruhe Institute of Technology (Germany)
  • Florian Faion, Karlsruhe Institute of Technology (Germany)
  • Antonio Zea, Karlsruhe Institute of Technology (Germany)

Special Session Contact