Course Description

From cs331b Special Topics in 3dRR

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'''Piazza:''' [http://piazza.com/stanford/fall2013/cs331b/home link]  
'''Piazza:''' [http://piazza.com/stanford/fall2013/cs331b/home link]  
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'''Staff E-mail: '''cs331b-aut1314-staff 'at' lists 'dot' stanford 'dot' edu  
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'''Staff E-mail:&nbsp;'''cs331b-aut1314-staff 'at' lists 'dot' stanford 'dot' edu<br> <br> '''Course Description'''  
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<br> <br> '''Course Description'''  
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The course surveys recent developments in high level and 3D computer vision and will focus on reading recent research papers on topics related to 3D object recognition and representation, spatial inference, activity understanding, human vision and 3D perception. The course is inspired by a famous series of workshops (called 3d-RR) which have been offered during the International Conference in Computer Vision (ICCV) since 2007.  
The course surveys recent developments in high level and 3D computer vision and will focus on reading recent research papers on topics related to 3D object recognition and representation, spatial inference, activity understanding, human vision and 3D perception. The course is inspired by a famous series of workshops (called 3d-RR) which have been offered during the International Conference in Computer Vision (ICCV) since 2007.  

Revision as of 15:09, 25 September 2013

Piazza: link

Staff E-mail: cs331b-aut1314-staff 'at' lists 'dot' stanford 'dot' edu

Course Description

The course surveys recent developments in high level and 3D computer vision and will focus on reading recent research papers on topics related to 3D object recognition and representation, spatial inference, activity understanding, human vision and 3D perception. The course is inspired by a famous series of workshops (called 3d-RR) which have been offered during the International Conference in Computer Vision (ICCV) since 2007.


Requirements:

Present 1-2 set of papers
Read papers and participate at class discussion during paper presentations
Course project: replicate existing methods or implement new research ideas.


Grading policy:

  • Class participation & discussion: 20%
  • Paper presentation (quality, clarity, depth, etc.): 30%
  • Course project (quality of the project presentation, work, writing, etc): 50%
    • progress report: 5%
    • final report: 35%
    • presentation: 10%


Late policy project:

  • If 1 day late, 25% off the grade for the project
  • If 2 days late, 50% off the grade for the project
  • Zero credits if more than 2 days


Prerequisites:

  • Some experience in research with one of the following fields: computer vision (CS 231), image processing, computer graphics, machine learning (CS 229).
  • MATLAB or equivalent programming experience is expected.
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