Computational photography is the convergence of computer graphics, computer vision, optics, and imaging. Its role is to overcome the limitations of traditional cameras, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world.

This course provides an overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms. Then we will continue to learn about the physical and computational aspects of tasks such as 3D scanning, coded photography, lightfield imaging, time-of-flight imaging, VR/AR displays, and computational light transport. Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see below skin.

The course has a strong hands-on component, in the form of seven homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class, by both acquiring their own images of indoor and outdoor scenes and developing the computational tools needed to extract information from them. Example homework includes building end-to-end HDR imaging pipelines and structured light scanners. For their final projects, students will have the choice to use modern sensors and other optical instrumentation provided by the instructors (lightfield cameras, time-of-flight sensors, projectors, laser sources, and so on).

Cross-listing: This is both an advanced undergraduate and introductory graduate course, and it is cross-listed as 15-463 (for undergraduate students), 15-663 (for Master's students), and 15-862 (for PhD students). Please make sure to register for the section of the class that matches your current enrollment status.

This course requires familiarity with linear algebra, calculus, programming, and doing computations with images. In particular, either of the following courses can serve as proof that you satisfy these prerequisites:

• 16-385 Computer Vision, OR
• 16-720 Computer Vision, OR
• 15-462 Computer Graphics, OR
• 18-793 Image and Video Processing.

If you want to enroll but have not taken any of the above courses, please make sure to contact the instructor! We make a lot of exceptions each year, on a case-by-case basis. The course does not require prior experience with photography or imaging.

Readings will be assigned primarily from relevant papers. Readings will be posted at the last slide of each lecture.

Additionally readings may be assigned from the following textbooks, which can also be useful references in general. All of them are available online from the CMU library:

• Computer Vision: Algorithms and Applications, by Richard Szeliski.
• Multiple View Geometry in Computer Vision, by Richard Hartley and Andrew Zisserman.
• Computer Vision: A Modern Approach, by David Forsyth and Jean Ponce.
• Foundations of 3D Computer Graphics, by Steven Gortler.
• Digital Image Processing, by Rafael Gonzalez and Richard Woods.
• Photography, by Barbara London and John Upton.

Lectures will take place on Zoom. See the course's Canvas page for Zoom meeting links.

Your final grade will be made up of:

• Six two-week homework assignments (60%).
• Optional seventh homework assignment (10%).
• Final project (35%).
• Class participation (5%).

Homework assignments: All homework assignments will have a programming component and a photography component, where students will use a DSLR camera to capture and process their own images. The programming component of all assignments will be done in Python.

Bonus homework assignment: There will be a seventh optional homework assignment that can be used to make up for lost credit. It will involve using a physics-based graphics renderer to simulate computational photography techniques.

Late days: For the homework assignments, students will be allowed a total of five free late days. Any additional late days will each incur a 10% penalty.

Collaboration policy: Students are encouraged to work in groups but each student must submit their own work. This includes: writing your own code, building your own imaging setups, taking your own photographs, and producing your own writeup. If you work as a group, include the names of your collaborators in your writeup. You absolutely should not share or copy code. Additionally, you should not use any external code unless explicitly permitted. Plagiarism is strongly prohibited and will lead to failure of this course.

Submitting homeworks: We will use Canvas for submitting and grading homeworks.

Final project: Details about the final project can be found here.

15-663, 15-862: Students taking 15-663 or 15-862 will be required to do a more substantial final project, as well as submit a longer paper describing their project.

Students are encouraged to obtain a digital camera for use in the course — any digital camera with manual controls should work. However, this is not a requirement, and about 50 camera kits (DSLR camera, lens, and tripod) kits will be provided by the instructors for students that do not have one.

Camera tutorial: To make it easier for students to use the camera kits, we have put together a camera tutorial.

Email: Please use [15463] in the title when emailing the teaching staff!

Office hours: Teaching staff will have regular office hours at the following times:

• Tuesdays 3:30 - 5:30 pm ET, Byeongjoo.
• Wednesdays 3:30 - 5:30 pm ET, Yannis.
• Thursdays 3:30 - 5:30 pm ET, Jenny.

All office hours will take place on Zoom. See the course's Canvas page for Zoom meeting links.

Feel free to email us about scheduling additional office hours.

Discussion: We will use Piazza for course discussion and announcements.

We are actively looking for students at all levels (undergraduates, MS, PhD) to help in projects on various aspects of computational photography, imaging, rendering, and graphics in general. If you are interested, please send Yannis an email (or talk to him in person in class).

You should also take a look at the imaging group website, to find more information about related projects active here at CMU.

Finally, you are welcome to attend the imaging group meetings during the semester. If you want to receive announcements about the group meetings, as well as other emails regarding photography and imaging research, ask Yannis to add you to the imaging group's mailing list.

Slides will be uploaded on this website before each lecture.

• 15-463 (15-663, 15-862), Fall 2019 (taught by Ioannis Gkioulekas)
• 15-463 (15-663, 15-862), Fall 2018 (taught by Ioannis Gkioulekas)
• 15-463 (15-663, 15-862), Fall 2017 (taught by Ioannis Gkioulekas)
• 15-463 (15-663, 15-862), Fall 2015 (taught by Kris Kitani)

• Computational Imaging and Display (Gordon Wetzstein, Stanford)
• Computational Photography (Derek Hoiem, UIUC)
• Computational Photography (James Hays, Brown)
• Computational Photography (Ramesh Raskar and Jack Tumblin, SIGGRAPH course)
• Digital and Computational Photography (Fredo Durand, MIT)
• Introduction to Computational Photography (Oliver Cossairt, Northwestern)
• Introduction to Visual Computing (Kyros Kutulakos, University of Toronto)
• Visual Computing Systems (Kayvon Fatahalian, CMU)

The materials for this course have been pieced together from many different people and places. Special thanks to the following colleagues for sharing their course materials or making them available online (in alphabetical order): Supreeth Achar, Andrew Adams, Amit Agrawal, Michael Brown, Oliver Cossairt, Fredo Durand, Alyosha Efros, Kayvon Fatahalian, Steven Gortler, Mohit Gupta, Sam Hasinoff, James Hays, Hugues Hoppe, Ivo Ihrke, Kris Kitani, Kyros Kutulakos, Douglas Lanman, Jaako Lehtinen, Anat Levin, Marc Levoy, Steve Marschner, Srinivasa Narasimhan, Shree Nayar, Ren Ng, Matthew O'Toole, Sylvain Paris, Ravi Ramamoorthi, Ramesh Raskar, Aswin Sankaranarayanan, Robert Sumner, Richard Szeliski, Gavriel Taubin, James Tompkin, Gordon Wetzstein, Todd Zickler. Special thanks to Jiatian (Caroline) Sun for developing the renderer used in some of the homework assignments. Individual slides and homework assignments include their own credits.