Schedule and Syllabus

Lectures are held on Tuesdays and Thursdays from 1:30pm to 2:50pm @ McMurtry Art & Art History, Rm 102.

Recitations are held on select Fridays from 12:30pm to 1:30pm @ Building 300, 300.

Students with Documented Disabilities: Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty. Unless the student has a temporary disability, Accommodation letters are issued for the entire academic year. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL:

This is the syllabus for the Fall 2022 iteration of the course.

Homework releases can be found on GitHub.

Event Type Date Description Lecture Materials
Lecture 1 Tuesday
September 27
Course introduction and Logistics
[1.1 What is Computer Vision]
[1.2 Computer Vision Applications]
I. Geometric Vision
Lecture 2 Thursday
September 29
Image Formation and Color
Demo notebook (Video on Canvas)
Recitation 1 Friday
September 30
Python/NumPy Review I
[Blank Notebook] [Filled Notebook]
Lecture 3 Tuesday
October 4
Geometric Primitives and Transformations
Lecture 4 Thursday
October 6
Pinhole Camera Model, Calibration
Recitation 2 Friday
October 7
Linear Algebra Review
HW0 Due Friday
October 7, 11:59pm
Homework #0 due
[Homework #0]
Lecture 5 Tuesday
October 11
Multi-view Geometry
Lecture 6 Thursday
October 13
Structure from Motion
Recitation 3 Friday
October 14
Panorama Review
HW1 Due Friday
October 14, 11:59pm
Homework #1 due
Camera + Calibration
[Homework #1]
II. Low-Level Vision
Lecture 7 Tuesday
October 18
Demo notebook (Video on Canvas)
Lecture 8 Thursday
October 20
Edges and Lines
Demo notebook (Video on Canvas)
HW2 Due Friday
October 21, 11:59pm
Homework #2 due
Panorama Stitching
[Homework #2]
Lecture 9 Tuesday
October 25
Local Features and Fitting
Demo notebook (Video on Canvas)
Lecture 10 Thursday
October 27
Feature Detectors and Descriptors
Demo notebook (Video on Canvas)
HW3 Due Friday
October 28, 11:59pm
Homework #3 due
[Homework #3]
Lecture 11 Tuesday
November 1
Motion and Optical Flow
Demo notebook (Video on Canvas)
III. Visual Pattern Recognition
Lecture 12 Thursday
November 3
Segmentation and Clustering
HW4 Due Friday
November 4, 11:59pm
Homework #4 due
Edge Detection
[Homework #4]
November 8
No class (Stanford Democracy Day)
Lecture 13 Thursday
November 10
Clustering: K-means and Mean-shift
HW5 Due Friday
November 11, 11:59pm
Homework #5 due
Tracking - optical flow
[Homework #5]
Lecture 14 Thursday
November 15
ML for CV overview 1/2
[14.1 ML For CV: A Brief Overview]
Lecture 15 Thursday
November 17
ML for CV overview 2/2
[15.1 ML For CV: A Brief Overview]
HW6 Due Friday
November 18, 11:59pm
Homework #6 due
Segmentation - Clustering
Homework #6]
IV. Advanced Topics
Lecture 16 Tuesday
November 29
Vision for Robotics & Self-driving
Lecture 17
Shyamal Buch
December 1
Understanding Videos
HW7 Due Friday
December 2, 11:59pm
Homework #7 due
Deep Learning - PyTorch
[Homework #7]
Lecture 18
Vincent Sitzmann
December 6
3D Deep Learning
Lecture 19
Guest Lecture (TBD)
December 8
CV Ethics