From cs331b Special Topics in 3dRR

Announcements

• Midterm reports of due this week! They should include any specifics we asked for in your project proposal feedback. You should also include any progress you've made, including preliminary results, datasets, and any issues you're having in implementing your project. I look forward to reading everybody's report!
• Project proposals are due this week! They should be in the format of project papers as outlined under "Resources," and should be around 1-2 pages. Please e-mail your submissions to the staff mailing list with the subject "cs331b - project proposal", and include your name in the e-mail body.
• We will be holding extra presentations outisde lecture. If you haven't done so already, go here and fill out when you will be available: link
• You should have received an e-mail about picking presentation topics. If not, I've posted the same information on Piazza. If you haven't received the e-mail, there's a chance you may not be on the mailing list for this class. If so, let your TA know, so he can add you.
• There is a Piazza for this class. Please sign up for it if you haven't already.
•  The course will start on September 23rd.

Course Description

Piazza: link

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

Course Title: Special Topics in Computer Vision—3D Representation and Recognition (3dRR)

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.