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teaching:gsoc2017 [2017/03/06 14:39] – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] balintbeteaching:gsoc2017 [2017/03/17 18:21] (current) – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] balintbe
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 **Task Difficulty:** The task is considered to be challenging, as it is still a hot research topic where general solutions do not exist. **Task Difficulty:** The task is considered to be challenging, as it is still a hot research topic where general solutions do not exist.
      
-**Requirements:** Good programming skills in C++ and basic knowl- +**Requirements:** Good programming skills in C++ and basic knowledge of CMake and ROS. Experience with PCL, OpenCV is prefered.
-edge of CMake. Experience with PCL, OpenCV is prefered.+
  
 **Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these. **Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these.




Prof. Dr. hc. Michael Beetz PhD
Head of Institute

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ai-office@cs.uni-bremen.de

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