Coursework

Role playing participation (55%)

Marks will be distributed equally across 11 paper discussions (5% per paper). All role-specific written components are due at midnight before the paper is discussed in class (i.e., Monday 11:59pm or Wednesday 11:59pm).

Course project (45%)

The course project consists of a research project with both a written report and a source code submission. Projects may be completed individually or in teams of up to two students. Submit one proposal and one final report per team. The expected workload should be proportional to team size.

Project proposal is due on Friday October 25 (5% of total grade). Final report is due at the end of classes (40% of total grade).

The final report grade takes into account your source code submission (code organization and documentation) and the report itself (appropriate format and length, abstract, introduction, related work, description of your method, quantitative and qualitative evaluation of your method, results, discussion & conclusion, bibliography).

You can work in teams of up to 2 students for the project. Submit only one proposal and final report for each team. The expected amount of work is relative to the number of team members, so if two teams work on a similar project, we'd expect less work from a smaller team.

The project proposal is a 1-2 page document that should contain the following elements: clear motivation of your idea, a discussion of related work along at least 3 scientific references (i.e., scientific papers not blog articles or websites), an overview of what exactly your project is about and what the final goals are, milestones for your team with a timeline and intermediate goals. After proposal submission, revisions may be suggested.

The final project report should look like a short (~6 pages) conference paper. We expect the following sections, which are standard practice for conference papers: abstract, introduction, related work, theory (i.e., your approach), analysis and evaluation, results, discussion and conclusion, references. Use the CVPR 2025 LaTeX template for your report. A detailed rubric can be found on BrightSpace under the final project report assignment.

Late policy

All homework is due at midnight on the due date. For role-specific homework, there will be a 30% deduction if you submit late, but before the start of that week's lecture (i.e., if you submit anytime between 12:01am and 12pm on Tuesday or Thursday). No homework will be accepted after the start of lecture.

If you need more time to submit your project proposal or final project report, you will need to discuss your timeline with the instructor and get approval at least 1 week before the posted due date.

ChatGPT policy

You may use any tools you find productive in preparing your reports. But you are responsible for any misrepresentations, inaccuracies, or plagiarism. If we find such defects in submitted work, we reserve the right to exact penalties, which may range from grade deductions to reporting academic misconduct depending on the severity of the offense.

Schedule and Syllabus

Week	Date	Description	Material	Readings	Event	Deadline
Week 1	Tue Aug 26	Lecture 1: Course intro	[slides]	Marc Levoy's course on digital photography
	Thu Aug 28	Lecture2: Digital photography	[slides]
Week 2	Tue Sep 2	Lecture 3: Pinholes and lenses	[slides]	Marc Levoy's course on digital photography
	Thu Sep 4	Lecture 4: Selected topics
Week 3		Imaging and Sound
	Tue Sep 9	The visual microphone: Passive recovery of sound from video	[Paper]
	Thu Sep 11	Dual-Shutter Optical Vibration Sensing	[Paper]
Week 4		Optical Coding
	Tue Sep 16	Light field photography with a hand-held plenoptic camera	[Paper]
	Thu Sep 18	DiffuserCam: lensless single-exposure 3D imaging	[Paper]
Week 5		Computational Light Transport
	Tue Sep 23	Femto-photography: capturing and visualizing the propagation of light	[Paper]
	Thu Sep 25	Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging	[Paper]
Week 6		Single-Photon Imaging
	Tue Sep 30	Passive inter-photon imaging	[Paper]
	Thu Oct 2	Quanta Burst Photography	[Paper]
Week 7		Single-Photon 3D Sensing
	Tue Oct 7	First-Photon Imaging	[Paper]
	Thu Oct 9	Opportunistic Single-Photon Time of Flight	[Paper]
Week 8	Tue Oct 14	Fall break
	Thu Oct 16	Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera	[Paper]
Week 9	Tue Oct 21	Project proposal presentation
	Thu Oct 23	Projects Q&A (on Zoom)
	Fri Oct 25					Project proposals due at 11:59pm
Week 10		Neural Representations & Computational Imaging I
	Tue Oct 28	Flying with Photons	[Paper]
	Thu Oct 30	Neural Inverse Rendering from Propagating Light	[Paper]
Week 11		Neural Representations & Computational Imaging II
	Tue Nov 4	NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images	[Paper]
	Thu Nov 6	Learning Lens Blur Fields	[Paper]
Week 12		TBD
	Tue Nov 4	Minimalist Camera/flutter shutter	[Paper]
	Thu Nov 13	Invited Talk
Week 13		Deep Optics
	Tue Nov 18	Deep Optics for Single-shot HDR Imaging	[Paper]
	Thu Nov 20	Curriculum learning for ab initio deep learned refractive optics	[Paper]
Week 14		Generative Computational Imaging
	Tue Nov 25	Generative Photography	[Paper]
	Thu Nov 27	Thanksgiving break
Week 15	Tue Dec 2	Invited talk:TBD
	Thu Dec 4	Project Q&A and debugging
Week 16	Tue Dec 9	Invited Talk:TBD
	Thu Dec 11	Project Q&A
	Sat Dec 20					Project reports due at 11:59pm

Related courses at Purdue and elsewhere

• Foundations of Computational Imaging (Charles Bouman, Purdue)
• Cameras, Images, and Statistical Inverse Problems (Stanley Chan, Purdue )
• Computational Imaging (David Lindell, University of Toronto)
• Introduction to Visual Computing (Kyros Kutulakos, University of Toronto)
• Computational Imaging and Display (Gordon Wetzstein, Stanford)
• Computational Photography (Ioannis Gkioulekas, CMU)
• Digital and Computational Photography (Fredo Durand, MIT)

Examples of course projects at other institutions

• Fall 2024 (Toronto)
• Fall 2023 (Toronto)
• Fall 2022 (Toronto)
• Winter 2022 (Stanford)
• Winter 2021 (Stanford)
• Winter 2020 (Stanford)

Acknowledgements

The course is adapted from CSC2529 at the University of Toronto by David Lindell. Some of the materials used in class build on that from other instructors, including Yannis Gkioulekas, Marc Levoy, Fredo Durand, Ramesh Raskar, Kyros Kutulakos, Shree Nayar, Paul Debevec, Matthew O'Toole and others, as noted in the slides. Feel free to use these slides for academic or research purposes, but please maintain all acknowledgments. This webpage is based on the website for CSC2529 at the University of Toronto.