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teaching:le-sem-perc-ws17 [2017/10/12 09:21]
balintbe
teaching:le-sem-perc-ws17 [2017/10/26 11:17]
balintbe
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 === Description === === Description ===
 +
 +Dr. Michael Suppa is a leading expert in robot perception and visiting professor at the University of Bremen. He worked as project manager and researcher at the Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) in the research areas: robotic exploration,​ 3-D vision, and data fusion. From 2009 until 2015, he was the Head of the Department Perception and Cognition, a recognized world leader on key robotic
 +research topics such as complex scene analysis, perception for resource-limited systems and robotic cognition. In March 2015 he co-founded Roboception,​ a DLR spin-off company devoted to advancing the State-of-the -Art in 3D sensors and vision. ​
 +
 +
 The lecture covers all important aspects of 3D semantic perception with a high focus on applicability and real world tasks. Some of the topics covered by the lecture: The lecture covers all important aspects of 3D semantic perception with a high focus on applicability and real world tasks. Some of the topics covered by the lecture:
   * Introduction to 3D   * Introduction to 3D
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   * and more   * and more
  
 +=== Assignments Schedule: ===
 +
 +|< 100% 20% 40% 40%>|
 +^**Assignment nr.**^**Handed out**^**Submission date**^
 +^A1^26th October^13th November 08:00 AM^
 +^A2^16th November^4th December 08:00 AM^
 +^A3^7th December^8th January 08:00 AM^
 +^A4^11th January^4th February 12:00 PM^
 +
 +
 +**Three out of the four assignments must be submitted on time to enter to oral exam. Assignments will not be graded, but they will form a basis for discussion during the oral exam**
 +
 +=== Course Schedule (subject to change): ===
  
-=== Preliminary Course Schedule (subject to change): ===+**The order and date of exercises is subject to change. Check back regularly.** ​
  
 |< 100% 35% 65%>| |< 100% 35% 65%>|
 ^Date^Activity^ ^Date^Activity^
 ^19.10 10:00-11:30 and 12:​30-14:​00^**Lecture**:​ Introduction to 3D and Acquisition of 3D data^ ^19.10 10:00-11:30 and 12:​30-14:​00^**Lecture**:​ Introduction to 3D and Acquisition of 3D data^
-^26.10 10:​00-11:​30^Excerise: Introduction to 3D^+^26.10 10:​00-11:​30^Exercise: Introduction to 3D^
 ^09.11 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ Features and Filters^ ^09.11 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ Features and Filters^
-^16.11 10:​00-11:​30^Excerise: 2D Keypoints and descriptors^ +^16.11 10:​00-11:​30^Exercise: 2D Keypoints and descriptors^ 
-^23.11 10:​00-11:​30^Excerise: 3D Keypoints and descriptors^+^23.11 10:​00-11:​30^Exercise: 3D Keypoints and descriptors^
 ^30.11 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ Objects I and II^ ^30.11 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ Objects I and II^
-^07.12 10:​00-11:​30^Excerise: Object recognition^ +^07.12 10:​00-11:​30^Exercise: Object recognition^ 
-^14.12 10:​00-11:​30^Excerise: Discussion of assignment^+^14.12 10:​00-11:​30^Exercise: Discussion of assignment^
 ^21.12 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ Scenes I and II^ ^21.12 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ Scenes I and II^
-^TBA^Excerise: Segmentation of kitchen scene^+^11.01 10:00-11:30^Exercise: Segmentation of kitchen scene^
 ^18.01 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ CNNs and Applications^ ^18.01 10:00-11:30 and 12:​30-14:​00^ **Lecture**:​ CNNs and Applications^
-^TBA^Excerise: Exercise using CNNs^+^25.01 10:00-11:30^Exercise: Exercise using CNNs^