This course is an introduction to concepts and applications in computer vision primarily dealing with geometry and 3D understanding.

Topics Include

• Cameras and projection models
• Low-level image processing methods such as filtering and edge detection
• Segmentation and clustering
• Shape reconstruction from stereo
• Depth estimation and optical/scene flow
• 6D pose estimation
• Object tracking

What You Need to Succeed

• A conferred bachelor’s degree with an undergraduate GPA of 3.0 or better
• Proficiency in Python, high-level familiarity in C/C++ . All class assignments will be in Python (and use numpy), but some of the deep learning libraries that you may want to use for your projects are written in C++. If you have a lot of programming experience but in a different language (e.g., C/C++/Matlab/Javascript) you will probably be fine.
• College calculus and linear algebra (e.g., MATH 19 or 41, MATH 51). You should be comfortable taking derivatives and understanding matrix vector operations and notation.
• Basic probability and statistics (e.g., CS109 or STATS116).You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc.
• Equivalent knowledge of CS131, CS221, or CS229. You should be familiar with basic machine learning or computer vision techniques.

What You Need To Get Started

Before enrolling in your first graduate course, you must complete an online application.

Don’t wait! While you can only enroll in courses during open enrollment periods, you can complete your online application at any time.

Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your mystanfordconnection account at any time.

Learn more about the graduate application process.

How Much It Will Cost

Learn more about tuition and fees.