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14th IEEE International Workshop on

Computational Cameras and Displays

Music City Center, Nashville TN

June 11, CVPR 2025

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Introduction

Computational photography has become an increasingly active area of research within the computer vision community. Within the few last years, the amount of research has grown tremendously with dozens of published papers per year in a variety of vision, optics, and graphics venues. A similar trend can be seen in the emerging field of computational displays – spurred by the widespread availability of precise optical and material fabrication technologies, the research community has begun to investigate the joint design of display optics and computational processing. Such displays are not only designed for human observers but also for computer vision applications, providing high-dimensional structured illumination that varies in space, time, angle, and the color spectrum. This workshop is designed to unite the computational camera and display communities in that it considers to what degree concepts from computational cameras can inform the design of emerging computational displays and vice versa, both focused on applications in computer vision.

The Computational Cameras and Displays (CCD) workshop series serves as an annual gathering place for researchers and practitioners who design, build, and use computational cameras, displays, and imaging systems for a wide variety of uses. The workshop solicits posters and demo submissions on all topics relating to computational imaging systems.

Previous CCD Workshops: CCD2024, CCD2023, CCD2022, CCD2021, CCD2020, CCD2018, CCD2017, CCD2016, CCD2014

Location: 205 A, (posters in Hall D)

Keynote Talks

Shree Nayar
Shree Nayar
Columbia
Shree K. Nayar is the T. C. Chang Professor of Computer Science at Columbia University. He heads the Columbia Vision Laboratory (CAVE), which develops computational imaging and computer vision systems. His research is focused on three areas - the creation of novel cameras that provide new forms of visual information, the design of physics-based models for vision and graphics, and the development of algorithms for understanding scenes from images. His work is motivated by applications in the fields of imaging, computer vision, robotics, virtual reality, augmented reality, visual communication, computer graphics and human-computer interfaces. Nayar received his PhD degree in Electrical and Computer Engineering from the Robotics Institute at Carnegie Mellon University. For his research and teaching he has received several honors including the David Marr Prize (1990 and 1995), the David and Lucile Packard Fellowship (1992), the National Young Investigator Award (1993), the NTT Distinguished Scientific Achievement Award (1994), the Keck Foundation Award for Excellence in Teaching (1995), the Columbia Great Teacher Award (2006), the Carnegie Mellon Alumni Achievement Award (2009), Sony Appreciation Honor (2014), the Columbia Engineering Distinguished Faculty Teaching Award (2015), the IEEE PAMI Distinguished Researcher Award (2019), the Funai Achievement Award (2021), and the Okawa Prize (2023). For his contributions to computer vision and computational imaging, he was elected to the National Academy of Engineering in 2008, the American Academy of Arts and Sciences in 2011, the National Academy of Inventors in 2014, and the Indian National Academy of Engineering in 2022.
Ioannis Gkioulekas
Ioannis Gkioulekas
CMU
Ioannis Gkioulekas is an associate professor in the Robotics Institute of Carnegie Mellon University. He works broadly in computer graphics and computer vision, focusing on computational imaging—the joint design of optics, electronics, and computation to create imaging systems with unprecedented capabilities. Technical keywords that often show up in his research include: interferometry, non-line-of-sight imaging, single-photon imaging, lidar, sonar, acousto-optics, physics-based rendering, differentiable rendering, volume rendering, Monte Carlo simulation. He has received the NSF Career Award, Sloan Research Fellowship, Bodossaki Distinguished Young Scientist Award, and best paper awards at CVPR 2019, CVPR 2024, and SIGGRAPH 2024.
Laura Waller
Laura Waller
UC Berkeley
Laura Waller leads the Computational Imaging Lab at UC Berkeley, which develops new methods for optical imaging, with optics and computational algorithms designed jointly. She holds the Ted Van Duzer Endowed Professorship and is a Senior Fellow at the Berkeley Institute of Data Science (BIDS), with affiliations in Bioengineering and Applied Sciences & Technology. Laura Waller earned her B.S., M.Eng., and Ph.D. degrees from the Massachusetts Institute of Technology, USA. She was a postdoctoral research associate at Princeton University, USA before joining the UC Berkeley. Waller has established herself as a visionary in the important new field of computational imaging through her pioneering work on phase retrieval from intensity measurements. She has also made advancements in Fourier ptychography, 3D imaging in scattering media and in imaging using a diffuser. In the past four years, Waller has generated ~$6M in research funding and published 27 journal papers and 6 patent applications. Her research has been recognized through over 100 invited talks, and she has contributed to the research community by serving on 44 conference committees (14 as chair) and as Associate Editor of IEEE Transactions on Computational Imaging. She has received many other awards, including the SPIE Early Career Achievement Award, the Chan-Zuckerberg Initiative Biohub Investigator Award, and the OSA Foundation’s Ivan P. Kaminow Outstanding Early Career Professional Prize. She is a Fellow of OSA and the American Institute for Medical and Biological Engineering. In 2021, she received OSA's Adolph Lomb Medal "for important contributions to the advancement of computational microscopy and its applications”, and in 2024 she received the Max Planck-Humboldt Medal for her outstanding achievements in computational microscopy.
Ren Ng
Ren Ng
UC Berkeley
Ren Ng is a professor in Electrical Engineering and Computer Science at the University of California, Berkeley. His research interests are in imaging, graphics, human and computer vision. Ren completed his Ph.D. in computer science at Stanford University, and received the ACM Doctoral Dissertation Award and Stanford's Arthur Samuel Award. Before Berkeley, Ren was founder and CEO of Lytro, Inc., which commercialized his Ph.D. research. Ren ran the company as CEO for 7 years, raising over $50M in investment, growing the company to 70 employees, and shipping the first consumer light field camera worldwide. Ren has received the SIGGRAPH Best Paper Honorable Mention, ECCV Best Paper Honorable Mention, Jim and Donna Gray Faculty Award for Undergraduate Teaching, Hellman Faculty Fellowship, Sloan Research Fellowship, IS&T Image Engineering Innovation Award, HIPA Photographic Research Award, PMDA Technical Achievement Award, R.I.T.'s Imaging Hall of Fame, the Selwyn Award from the Royal Photographic Society, MIT Tech Review's TR35 and Entrepreneur of the Year, Fast Company's 100 Most Creative People in Business, Silicon Valley Journal's 40 under 40, and is an Optica Fellow.

Invited Talks

Manasi Muglika
Manasi Muglikar
University of Zurich
Manasi is currently a Ph.D student advised by Prof. Davide Scaramuzza at Robotics and Perception Group, in the Department of Informatics, University of Zurich, and the Department of Neuroinformatics, University of Zurich and ETH Zurich. Manasi’s interests lie at the intersection of computer vision and computational photography with particular interest in developing algorithms for event-based cameras, and their applications in robotics and augmented reality. Manasi was recently featured in the NCCR Robotics campaign for her work on event cameras. Previously, Manasi graduated from Carnegie Mellon University with Masters in Electrical and Computer Engineering in 2018, where she was advised by Prof. Srinivasa Narasimhan. During her Ph.D, she also worked at the Camera Culture Group at MIT, advised by Prof. Ramesh Raskar.
Suyeon Choi
Suyeon Choi
Stanford University
Suyeon is currently a postdoctoral scholar at the Stanford Computational Imaging Lab, working with Prof. Gordon Wetzstein. He is broadly interested in developing new algorithms and optical systems at the intersection of graphics, optics, AI, and vision science. During my Ph.D., he developed holographic display systems incorporating machine learning towards next-generation VR/AR displays, and his thesis received the ACM SIGGRAPH Outstanding Dissertation Award Honorable Mention in 2025. His research has been supported by a Meta Research PhD Fellowship, a SPIE Optics and Photonics Education Scholarship, a Kwanjeong Scholarship, a Korean Government Scholarship, and a GPU gift from NVIDIA.
Florian Willomitzer
Florian Willomitzer
University of Arizona
Florian Willomitzer an Associate Professor at the Wyant College of Optical Sciences and directs the Computational 3D Imaging and Measurement (3DIM) Lab. He graduated from the University of Erlangen-Nuremberg, Germany, where he received his Ph.D. degree with honors (‘summa cum laude’) in 2017. During his doctoral studies, he investigated physical and information-theoretical limits of optical 3D-sensing for medical imaging and industrial inspection, and implemented sensors that operate close to these limits. In the 3DIM Lab, Prof. Willomitzer and his team work on novel methods to image hidden objects through scattering media or around corners, high-resolution holographic displays, unconventional methods for precise VR eye-tracking, and the implementation of high-precision metrology methods in low-cost mobile handheld devices. Moreover, the group develops novel time-of-flight and structured light imaging techniques working at depth resolutions in the 100μm-range. Prof. Willomitzer serves/served as Chair and Committee Member of several Optica COSI conferences, Optics Chair of the 2022 IEEE ICCP conference, Chair and Host of the Optica Incubator on Imaging Through 100 Scattering Lengths, Committee member of Optica FiO conferences and as reviewer for Nature, Optica (OSA), SPIE, IEEE and CVPR. He is a recipient of the NSF CRII grant, winner of the Optica 20th Anniversary Challenge, and his Ph.D. thesis was awarded with the Springer Theses Award for Outstanding Ph.D. Research.
Jen-Hao (Rick) Chang
Jen-Hao (Rick) Chang
Apple
Rick is currently a research scientist at Apple where he focuses on neural rendering, generative models for sequences, and computational imaging and displays. His research has been incorporated into various Apple products, including Siri, Scribble on iPad, QuickPath keyboard on iOs, and watchOS. His current research interests are broadly in generative models, computational photography, and novel sensors. Rick obtained his PhD from the ECE Department at Carnegie Mellon University, where he worked with Prof. Vijayakumar Bhagavatula and Prof. Aswin Sankaranarayanan on computational photography and displays, computer vision, and machine learning. He received hs M.S. and B.S. degrees in electrical engineering from National Taiwan University, where he worked with Prof. Tian-Li Yu, and Dr. Yu-Chiang Frank Wang on image processing.

Schedule

Time (Nashville local) Session
8:45 - 9:00 Welcome / Opening Remarks
9:00 - 9:30 Keynote by Ioannis Gkioulekas
A ray tracer for physics
9:30-9:50 Invited Talk by Manasi Muglikar
Computational imaging with event cameras
9:50 - 10:10 Invited Talk by Suyeon Choi
Design of Holographic Display Systems Based on Artificial Intelligence
10:10 - 10:40 Morning Break
10:40 - 11:10 Keynote by Shree K. Nayar
Can a Camera be Self-Sustaining?
11:10 - 11:25 Spotlight presentations
11:25 - 12:45 Poster Session
12:45 - 13:50 Lunch break
13:50 - 14:20 Keynote by Laura Waller
Computational Aberration Correction
14:20 - 14:40 Invited Talk by Rick Chang
Learning a good 3D representation via flow matching
14:40 - 15:00 Invited Talk by Florian Willomitzer
Coherent Computational Imaging with Synthetic Waves
15:00 - 15:30 Afternoon Break
15:30 - 16:00 Keynote by Ren Ng
Hi olo! Meet saq and mal
16:00 - 16:45 Panel discussion: Ioannis Gkioulekas, Shree K. Nayar, Laura Waller, Ren Ng
16:45 - 16:55 Closing Remarks

Posters & Spotlights

ID Board Number Title Presenter
1 #179 BayesiaNF: Scalable Posterior Estimation for Bayesian Inverse Imaging Tianao Li
2 #180 Blending optimizations for segmented content in headset-free multifocal displays Ahmed Othman
3 #181 Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries Wei Xu
4 #182 Coherent Optical Modems for Full-Wavefield Lidar Parsa Mirdehghan
5 #183 Dense Dispersed Structured Light for Hyperspectral 3D Imaging fo Dynamic Scenes Suhyun Shin
7 #185 Dual Exposure Stereo for Extended Dynamic Range 3D Imaging Juhyung Choi
8 #186 Event Ellipsometer: Event-based Mueller-Matrix Video Imaging Ryota Maeda
9 #187 Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation Tianfu Wang
10 #188 Focal Split: Untethered Snapshot Depth from Differential Defocus Junjie Luo
11 #189 Gaussian Wave Splatting for Computer-Generated Holography Suyeon Choi
12 #190 Hardware Coding Function Design for Compressive Single-photon 3D Cameras David Parra
13 #191 NeuSee: Neural Imaging to See Through Dazzle Xiaopeng Peng
14 #192 Pixel-aligend RGB-NIR imaging for robot vision Jinnyeong Kim
15 #193 Practical single photon color imaging Tianyi Zhang
16 #194 PS-EIP: Robust Photometric Stereo Based on Event Interval Profile Kazuma Kitazawa
17 #195 Rapid wavefront shaping using an optical gradient acquisition Sagi Monin
18 #196 Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation Jingxi Chen
19 #197 Seeing A 3D World in A Grain of Sand Yufan Zhang
20 #198 Event fields: Capturing light fields at high speed, resolution, and dynamic range Ziyuan Qu
21 #199 Solving partial differential equations in participating media Ioannis Gkioulekas
22 #200 Spectrum from Defocus: Fast, Compact, and Interpretable Hyperspectral Imaging Mehmet Kerem Aydin
23 #201 Text-Guided Image Restoration via a Unified Plug-and-Play Diffusion Framework Zihui Wu
24 #202 Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields Runfeng Li
25 #203 Vision with Heat and Light Mani Ramanagopaln
26 #204 Opportunistic Single-Photon Time of Flight Mian Wei

Workshop Chairs

Computational Cameras and Displays Workshop - June 11, 2025