Lectures are held on Tuesdays and Thursdays from 1:30pm to 3:00pm @ Hewlett 201, also Zoom.
Recitations are held on select Fridays from 12:15pm to 1:15pm @ Building 200230, also Zoom .
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: 7231066, URL: https://oae.stanford.edu/).
Homework releases can be found on GitHub.
Event Type  Date  Description  Prerecorded Materials  Live Lecture Materials  

Lecture 1 
Tuesday September 21 
Course introduction and Logistics 
[1.1 What is Computer Vision]
[1.2 Computer Vision Applications] 
Logistics  
I. LowLevel Vision (this part will be flipped classroom)  
Lecture 2 
Thursday September 23 
Photometric Image Formation 
[2.1 Images, Sampling and Quantization]
[2.2 Color Physics] [2.3 Color Matching] [2.4 Color Constancy] 
Notebook


Recitation 1 
Friday September 24 
Python/NumPy Review I 

Lecture 3 
Tuesday September 28 
Filters 
[3.1 Linear Systems]
[3.2 Convolution and Correlation] 
Notebook  
Lecture 4 
Thursday September 30 
Edges 
[4.1 Edge Detection Overview]
[4.2 Image gradients] [4.3 A simple edge detector] [4.4 Sobel edge detector] [4.5 Canny edge detector] [4.6 Hough Transform for Line Detection] 
Notebook  
Recitation 2 
Friday October 1 
Linear Algebra Review 

HW0 Due 
Friday October 1, 11:59pm 
Homework #0 due Basics 
[Homework #0]  
Lecture 5 
Tuesday October 5 
Features and Matching 
[5.1 Ransac] [5.2 Local invariant features] [5.3 Harris Corner Detector] 
Notebook  
Lecture 6 
Thursday October 7 
Features and Matching 
[6.1 Scale invariant keypoint detection] [6.2 SIFT] [6.3 HoG] 
Notebook  
Recitation 3 
Friday October 8 
Panorama 
Slides  
HW1 Due 
Friday October 8, 11:59pm 
Homework #1 due Filters 
[Homework #1]  
Lecture 7 
Tuesday October 12 
Optical Flow 
[7.1 Optical Flow] [7.2 LukasKanade method] [7.3 Pyramids for Large Motion] [7.4 HornSchunk method] [7.5 Applications] 

II. Geometric Vision (this part will be live)  
Lecture 8 
Thursday October 14 
Geometric Image Formation [Why Geometric Vision Matters] [Geometric Primitives in 2D & 3D] [2D & 3D Transformations] 
Slides 

Recitation 4 
Friday October 15 
Python/NumPy Review II 

HW2 Due 
Friday October 15, 11:59pm 
Homework #2 due Edge Detection 
[Homework #2]  
Lecture 9 
Tuesday October 19 
Pinhole Camera Model [From 3D to 2D: the Pinhole Camera Model] [Properties of Perspective] [Monocular cues for 3D] 
Slides 

Lecture 10 
Thursday October 21 
Camera Calibration [Camera Calibration: Estimating intrinsics K] [Pose Estimation: Estimating extrinsics [Rt]] 
Slides 

HW3 Due 
Friday October 22, 11:59pm 
Homework #3 due Tracking/Flow 
[Homework #3]  
Lecture 11 
Tuesday October 26 
Multiview Geometry [Triangulation] [Epipolar geometry] [Stereo] 
Slides 

Lecture 12 
Thursday October 28 
Structure from Motion [SfM Problem Definition] [Finding 2D Correspondences] [Solving for Cameras and 3D Points] 
Slides 

HW4 Due 
Friday October 29, 11:59pm 
Homework #4 due Panorama Stitching 
[Homework #4]  
III. Visual Pattern Recognition (this part will be live)  
Tuesday November 2 
No class (Stanford Democracy Day) 

Lecture 13 
Thursday November 4 
Segmentation 
[13.1 Introduction] [13.2 Gestalt theory] [13.3 Agglomerative clustering] [13.4 Oversegmentation] 

HW5 Due 
Friday November 5, 11:59pm 
Homework #5 due Camera + Calibration 
[Homework #5]  
Lecture 14 
Tuesday November 9 
Clustering 
[14.1 Kmeans] [14.2 Meanshift] 

Lecture 15 
Thursday November 11 
Supervised & Deep Learning 
[15.1 ML For CV: A Brief Overview] 

IV. Advanced Topics (this part will be live)  
Lecture 16 
Tuesday November 16 
Understanding Videos 
[16 Understanding Videos] 

Lecture 17 
Thursday November 18 
Vision for Robotics & Selfdriving 
[17 SelfSupervised 3D Vision] 

Lecture 18 Guest Lecture by Vincent Sitzmann 
Tuesday November 30 
Selfsupervised Scene Representation Learning 

Lecture 19 Guest Lecture by Anna Bethke 
Thursday December 2 
The FATE of AI Ethics: How to research AI in a fair, accountable, transparent, explainable, and ethical manner 