Welcome to RBE/CS549 course at WPI. The official name of the course is Computer Vision but a more apt title would be Classical and Deep Learning Approaches for Geometric Computer Vision since it is more descriptive of what the course has to offer. This course is taught by Prof. Nitin J. Sanket. Please sign up for Piazza if you haven't done so already.

All the class announcements will be made through Piazza. Please use Piazza to contact TAs. Please do NOT contact the TA's or the Instructor via email unless it's an emergency, and do NOT contact the TA's on any social media platform such as Facebook or WhatsApp (please respect their privacy) regarding course content. If you want to have a chat about our research, feel free to reach out to Prof. Sanket after class.

All the student reports will be released publicly online to enable better learning experience for others. We will be also announce top three submissions for each announcement on the wesbite. All the projects and homeworks are to be submitted using Canvas. If you find any errors/typo in the course github website, please edit the markdown '.md' file and send a 'pull request'. If you don't know how to use pull request, please check out this tutorial, alternatively post on Piazza.

Homework 0 (HW0) has been released on Aug 14, 2022 and is due on Aug 28, 2022. The knowledge from HW0 is a pre-requisite for the class.

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What is this course about?


This is an advanced course on graduate computer vision and computational photography. This course will explore image formation, image features, image segmentation, image stitching, image recognition, motion estimation, 3D point clouds and will touch upon basics of augmented reality. This course is designed to showcase how to model classical 3D geometry problems using Deep Learning.

Team

Prof. Nitin J. Sanket

Instructor
he/him/his

Dr. Lening Li

Teaching Assistant
he/him/his

Pre-requisities


Programming proficiency in Python 3, experience with Linux and completion of HW0 is a hard pre-requisite. We assume that the students are proficient with basic Linear Algebra, calculus and machine learning to the level of an undergraduate class.

Office Hours


Expectations


This course is fast-paced and has high-expectations of time and effort commitment from students. The students are expected to complete projects on time since the concepts build on top of each other and it is very easy to fall behind in this course. Please understand the concepts well, ask questions, use Piazza heavily and come to office hours. Help your peers out, but do not plagiarize.

Software Environment


We will use Python 3 as the programming platform throughout this course along with packages from OpenCV, PyTorch, Numpy, Scikit and Matplotlib. We officially support the server/cluster only. Feel free to use VirtualMachine if you have a Mac/Windows system. For most assignments, there will be a starter code released in PyTorch.

Assignments


All projects are to be done in groups of TWO whereas homeworks should be done INDIVIDUALLY. However, we encourage you to discuss with your peers and not cheat. The Midterm exam is to be done INDIVIDUALLY and no discussion is allowed. For clarifications, post a private post on Piazza. For further details, read the Collaboration Policy and Honor Code below.

Assignment Name With Link Deadline Grade Percentage
HW0: Alohomora Sunday, Aug 28, 2022 at 11:59:59 PM (Individual Submissions) 5
P1: My AutoPano Thursday, Sept 15, 2022 at 11:59:59 PM (Group Submissions) 12
P2: FaceSwap Saturday, Oct 01, 20222 at 1:59:59PM, , 2022 at 11:59:59 PM (Group Submissions) 12
HW1: AutoCalib Monday, Oct 10, 2022 at 11:59:59 PM (Individual Submissions) 5
P3: Building Built In Minutes: SfM and NeRF Saturday, Nov 05, 2022 at 11:59:59 PM (Group Submissions) 18
P4: VIO Wednesday, Nov 30, 2022 at 11:59:59 PM (Group Submissions) 18
Midterm Exam Wednesday, Dec 07, 2022 at 11:59:59 PM (Individual Submissions) 25
HW2: Sexy Semantic Mapping Friday, Dec 16, 2022 at 11:59:59 PM (Individual Submissions) 5


Class Slides


Class Number Class Date Slides/Resources Class Topic(s)
1 Aug 24 2022 Slides, Video: Introduction and Logistics
2 Aug 25 2022 Slides, Video: What is a Robot? And Interpreting Color
3 Aug 29 2022 Slides, Video: More Color And Image Filtering (Space)
4 Sept 01 2022 Slides, Video: Image Filters in Frequency Domain And Features
5 Sept 08 2022 Slides, Video: Image Features and Descriptors
6 Sept 12 2022 Slides, Video: Advanced Features
7 Sept 15 2022 Slides, Video: Image Stitching And Deep Feature Matching
8 Sept 19 2022 Slides, Video: "Fake 3D"
9 Sept 22 2022 Slides, Video: Deep Learning and Neural Networks
10 Sept 26 2022 Slides, Video: NN Tuning, GANs, Bayesian NNs
11 Sept 29 2022 Slides, Video: Camera Models
12 Oct 03 2022 Slides, Video: Depth From Monocular Single View
13 Oct 06 2022 Slides, Video: Epipolar Geometry and Two-View Stereo
14 Oct 10 2022 Slides, Video: Triangulation and PnP
15.1 Oct 24 2022 Slides, Video: Depth From Moving Monocular Camera (Bundle Adjustment)
15.2 Oct 24 2022 Slides, Video: Factor Graphs And TagSLAM
16 Oct 27 2022 Slides, Video: Neural Rendering
17 Oct 31 2022 Slides, Video: More NeRFs And Depth From Stereo
18 Nov 03 2022 Slides, Video: Optical Flow and Velocities
19 Nov 10 2022 Slides, Video: Learned Stereo Depth And Flow
20 Nov 14 2022 Slides, Video: Learned Monocular Depth And Visual Inertial Odometry Basics
21 Nov 17 2022 Slides, Video: Visual Inertial Odometry Details
22 Nov 21 2022 Slides, Video: LIDAR Depth Fusion
23 Nov 28 2022 Slides, Video: Image Segmentation And Instance Segmentation
24 Dec 01 2022 Slides, Video: More Segmentation
25 Dec 05 2022 Slides, Video: Object Detection And Event Cameras
26 Dec 08 2022 Slides, Video: Motion Segmentation
27 Dec 12 2022 Slides, Video: Depth From Focus And Defocus
28 Dec 15 2022 Slides, Video: Fancy Deep Learning And Recap


Syallbus


Singular Value Decomposition, Linear Least Squares, Random Sample Consensus, Image Acquisition, Color Spaces, Gaussians, Color Segmentation, Expectation Maximization, Gaussian Mixture Model, Convolution, Filtering in images, Corner and Edge Detection, Projective Geometry, Camera Model, Feature Matching, Homography, Classifiers, Optical Flow, Fundamental and Essential Matrix, Epipolar Geometry, Perspective-n-points, Bundle Adjustment, Fiducial markers, Factor graphs, Pose Graph Optimization, Visual Odometry, Structure from Motion, RGB-D reconstruction, perceptron, backpropagation, convolutional neural networks, deep learning.

Piazza and ELMS


If you haven't done so already, register yourself on Piazza with your wpi email. We'll be using Piazza for all announcements and discussions. Please use Piazza to contact the instructor/TAs (Feel free to use private posts on Piazza to contact me/TAs). Please do NOT contact the instructor/TAs via email unless it's an emergency, and do NOT contact the instructor/TAs on any social media platform such as Facebook or WhatsApp (please respect our privacy) regarding course content. If you want to have a chat about research, chat with Prof. Sanket after class.

All the assignments will be released on this website page.

All the assignments are to be submitted using Canvas. If you find any errors/typo in the course github website, please edit the required markdown '.md' file and send a 'pull request'. If you don't know how to use pull request, please check out this tutorial.

Submission Policy


The submissions are made through Canvas (unless otherwise specified) with the name HWk_DirID.zip (for homeworks, Pk_DirID.zip for projects) (where k is the assignment number, for e.g., for homework 0, this would be HW0_DirID.pdf). Here, DirID is your directory ID, i.e., the first part of your wpi email address. For e.g., if your wpi email address is ABCD@wpi.edu, then your DirID is ABCD. Keep your submissions professional, grammatically correct without spelling mistakes. Do not use slangs and chat shorthands on your submissions. You'll get 25% grade penalty for not following the submission guidelines.

Late Submission Policy


This course moves quickly, and concepts will build on top of each other. Therefore it's very important to keep up with the material. To encourage this, late assignments are penalized for 25% per day after the due date. But life is unpredictable; we all need a break sometimes. So, we allow you seven late days, to spend on any assignment(s) except the midterm exam and the final project (P4). You may submit an assignment late (after the due date) using a late day without any penalty. Think of a late day as pushing the deadline back by a day. So, to get full credit on a 2-days-late assignment, you'd need to use two late days. Late days can only be spent as full days (i.e., you can't use only half a late day for an assignment you submit 12 hrs late). If you are using a late day, mention it in the title of your submission as "USING X LATE DAY(S)" and post a comment on Canvas about the usage of a late day. We expect you to keep track of number of late days you have remaining and notify us of usage of one or more late days. We will default for penalty if we don't see the mention of late day usage as a comment on ELMS on that particular assignment. Again, If you are using a late day, mention it in the title of your submission as "USING X LATE DAY(S)".

Collaboration Policy and Honor Code


Collaboration is encouraged, but one should know the difference between collaboration and cheating. Cheating is prohibited and will carry serious consequences. Cheating may be defined as using or attempting to use unauthorized assistance, material, or study aids in academic work or examinations. Some examples of cheating are: collaborating on an take-home exam or homework unless explicitly allowed; copying homework; handing in someone else's work as your own; and plagiarism. You are welcome to collaborate with your peers on Piazza and in person. However it's important that the work you submit is an expression of your understanding, and not merely something you copied from a peer. So, we place strict limits on collaboration: Firstly, you must clearly cite your collaborators by name at the top of your report. This includes Piazza posts reference. You may not share or copy each other's code. You can discuss how your code works, and the concepts it implements, but you can't just show someone your code. You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose points for using external sources.) You may not use any service that involves payment, and you must clearly and explicitly cite all outside sources and materials that you made use of. We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the University's policies on academic dishonesty. Instances will be dealt with harshly and typically result in a failing course grade. Unless otherwise specified, you should assume that that the UMD Code of Academic Integrity applies. Unless otherwise specified, you should assume that that the WPI Code of Academic Integrity applies.

Reference Books


All concepts will be covered in class lecture, and in the lecture notes. However, we also recommend the following books as good references: Furthermore, refer to the slides from courses in the acknowledgement section.

Acknowledgements


This course is developed by being inspired by adapting some of the best parts of each of the courses at multiple universities and these resources are linked below and you are encouraged to look at their content to learn from them as well. The goal of this course is to be the best computer vision and robotics perception course in the world.

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