no code implementations • ECCV 2020 • Connor Henley, Tomohiro Maeda, Tristan Swedish, Ramesh Raskar
Hidden objects attenuate light that passes through the hidden space, leaving an observable signature that can be used to reconstruct their shape.
no code implementations • ICCV 2023 • Varun Sundar, Andrei Ardelean, Tristan Swedish, Claudio Bruschini, Edoardo Charbon, Mohit Gupta
As an added benefit, our projections provide camera-dependent compression of photon-cubes, which we demonstrate using an implementation of our projections on a novel compute architecture that is designed for single-photon imaging.
no code implementations • 21 May 2021 • Subhash Chandra Sadhu, Abhishek Singh, Tomohiro Maeda, Tristan Swedish, Ryan Kim, Lagnojita Sinha, Ramesh Raskar
Time of flight based Non-line-of-sight (NLOS) imaging approaches require precise calibration of illumination and detector positions on the visible scene to produce reasonable results.
no code implementations • ICCV 2021 • Tristan Swedish, Connor Henley, Ramesh Raskar
We recover high-frequency information encoded in the shadows cast by an object to estimate a hemispherical photograph from the viewpoint of the object, effectively turning objects into cameras.
no code implementations • 12 Oct 2019 • Tomohiro Maeda, Guy Satat, Tristan Swedish, Lagnojita Sinha, Ramesh Raskar
Seeing around corners, also known as non-line-of-sight (NLOS) imaging is a computational method to resolve or recover objects hidden around corners.
no code implementations • 9 Oct 2019 • Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
Recently, there has been the development of Split Learning, a framework for distributed computation where model components are split between the client and server (Vepakomma et al., 2018b).
no code implementations • 5 Oct 2019 • Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders.
no code implementations • 14 May 2019 • Ramesh Raskar, Praneeth Vepakomma, Tristan Swedish, Aalekh Sharan
We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data.
no code implementations • 8 Dec 2018 • Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, Abhimanyu Dubey
We survey distributed deep learning models for training or inference without accessing raw data from clients.
1 code implementation • 3 Dec 2018 • Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar
Can health entities collaboratively train deep learning models without sharing sensitive raw data?
1 code implementation • 30 Oct 2018 • Zhijing Jin, Tristan Swedish, Ramesh Raskar
Over the recent years, there has been an explosion of studies on autonomous vehicles.
no code implementations • ICCV 2017 • George Leifman, Dmitry Rudoy, Tristan Swedish, Eduardo Bayro-Corrochano, Ramesh Raskar
In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing videos that contain a depth map (RGBD) on a 2D screen.