Massachusetts Institute of Technology

Mitsubishi Electric Research Laboratories, Inc.

Facebook, Inc.

Facebook Technologies, LLC

Nokia Technologies Oy

Other

Samsung Electronics Co., Ltd.

University of California

Nokia Corporation

The General Hospital Corporation

Georgia Tech Research Corporation

Meta Platforms Technologies, LLC

Michigan State University

Mitsubishi Denki Kabushiki Kaisha

Mitsubishi Electric Corporation

Ramesh Raskar

Achuta Kadambi

Amit Kumar Agrawal

Jeroen Van Baar

Paul A Beardsley

Ayush Bhandari

Barmak Heshmat Dehkordi

Kartik Venkataraman

Douglas Robert Lanman

Paul H Dietz

Gordon Wetzstein

Matthew Waggener Hirsch

Vage Taamazyan

Christopher Barsi

Ahmad Byagowi

Andreas Udo Velten

Supreeth Krishna Rao

Agastya Kalra

Pranav Mishra

Rajeswari Kannan

Sai Sri Sathya

Vitor Pamplona

Boxin Shi

Albert Redo-Sanchez

Thomas H Willwacher

Shree Kumar Nayar

Ashok Veeraraghavan

Ankit Mohan

Hisham Bedri

Matthew Everett Lawson

Adrian Dumitru Ilie

Otkrist Gupta

Kok-Lim Low

Gregory F Welch

Jingyi Yu

Tristan Swedish

Karin Roesch

Clifton L Forlines

Jay E Thornton

Wolfgang Heidrich

Zhang Jia

Neeraj Choubey

Andrew Hamilton Coward

Yun Hee Kim

Szymon Jakubczak

Shinsaku Hiura

Anshuman Das

Yuanzhen Li

Ik Hyun Lee

Ethan Kroll Gordon

Guy Satat

Rohit Pandharkar

Gordon Moseley Andrews

Johnny Chung Lee

Jack Tumblin

Rogerio Schmidt Feris

Gregory L Charvat

Vidya Raghavan Setlur

Darren L Leigh

Yuichi Taguchi

Srikumar Ramalingam

Alex Olwal

Ravi Shenoy

Albert Redo Sanchez

Byung-kwan Park

James Gregson

Rajiv Gupta

Richard C Waters

Adrian Andrew Dorrington

Jan Zizka

Felix Heide

Dirk Brinkman

Alok Ajay Mehta

Michael Lee Gleicher

Vaibhav Vaish

Vlad Branzoi

Mamoru Kato

Marie K Herring

Matthew Tancik

Ghulam Ahmed Kirmani

Praneeth Vepakomma

Lee Vincent Streeter

Justin Romberg

James E Davis

Vincent Lee

Quinn Smithwick

Grace Rusi Woo

Pritesh Sankhe

Henry Holtzman

John F Hughes

Ju Yong Chang

Jonathan Westhues

Dikpal N Reddy

Yong Zhao

Hyunsung Park

Nickolaos Savidis

Alireza Aghasi

Pushyami Rachapudi

A computer-implemented method for computing a prediction on images of a scene includes: receiving one or more polarization raw frames of a scene, the polarization raw frames being captured with a polarizing filter at a different linear polarization angle; extracting one or more first tensors in one or more polarization representation spaces from the polarization raw frames; and computing a prediction regarding one or more optically challenging objects in the scene based on the one or more first tensors in the one or more polarization representation spaces.

Systems and methods for transparent object segmentation using polarization cues

A system for collecting data for training a computer vision model for shape estimation includes: an imaging system configured to capture one or more images; and a processing system including a processor and memory storing instructions that, when executed by the processor, cause the processor to: receive one or more input images from the imaging system; estimate a pose of an object depicted in the one or more images; render a shape estimate from a 3-D model of the object posed in accordance with the pose of the object; and generate a data point of a training dataset, the data point including one or more images based on the one or more input images and a label corresponding to the one or more images, the label including the shape estimate.

Systems and methods for generating and using visual datasets for training computer vision models

A system for facilitating stress-adaptive virtual experience station includes a virtual display system and camera system coupled to a kinematic rig. An array of transducers coupled to the station interact with a user based on a feedback signal, configured for user health treatments, audio effects, or computational imaging techniques.

Methods, systems, apparatuses, and devices for facilitating stress-adaptive virtual exerience stations

A multi-modal sensor system includes: an underlying sensor system; a polarization camera system configured to capture polarization raw frames corresponding to a plurality of different polarization states; and a processing system including a processor and memory, the processing system being configured to control the underlying sensor system and the polarization camera system, the memory storing instructions that, when executed by the processor, cause the processor to: control the underlying sensor system to perform sensing on a scene and the polarization camera system to capture a plurality of polarization raw frames of the scene; extract first tensors in polarization representation spaces based on the plurality of polarization raw frames; and compute a characterization output based on an output of the underlying sensor system and the first tensors in polarization representation spaces.

Systems and methods for augmentation of sensor systems and imaging systems with polarization

One embodiment of the present application sets forth a wearable device that includes a display comprising a plurality of pixels and configured to emit light, and a micro-lens array located adjacent to the display, and configured to produce a light field by altering the light emitted by the display, where at least one of the display or the micro-lens array is configured to move from a first position to a second position that aligns a first pixel in the plurality of pixels relative to the micro-lens array.

Light field display tilting

A deep neural network may be trained on the data of one or more entities, also know as Alices. An outside computing entity, also known as a Bob, may assist in these computations, without receiving access to Alices' data. Data privacy may be preserved by employing a 'split' neural network. The network may comprise an Alice part and a Bob part. The Alice part may comprise at least three neural layers, and the Bob part may comprise at least two neural layers. When training on data of an Alice, that Alice may input her data into the Alice part, perform forward propagation though the Alice part, and then pass output activations for the final layer of the Alice part to Bob. Bob may then forward propagate through the Bob part. Similarly, backpropagation may proceed backwards through the Bob part, and then through the Alice part of the network.

Secure training of multi-party deep neural network

A light source may illuminate a scene that is obscured by fog. Light may reflect back to a time-resolved light sensor. For instance, the light sensor may comprise avalanche photodiodes that are not single-photon sensitive. The light sensor may perform a raster scan. The imaging system may determine reflectance and depth of the fog-obscured target. The imaging system may perform a probabilistic algorithm that exploits the fact that times of arrival of photons reflected from fog have a Gamma distribution that is different than the Gaussian distribution of times of arrival of photons reflected from the target. The imaging system may adjust frame rate locally depending on local density of fog, as indicated by a local Gamma distribution determined in a prior step. The imaging system may perform one or more of spatial regularization, temporal regularization, and deblurring.

Methods and apparatus for improved imaging through scattering media

A distributed deep learning network may prevent an attacker from reconstructing raw data from activation outputs of an intermediate layer of the network. To achieve this, the loss function of the network may tend to reduce distance correlation between raw data and the activation outputs. For instance, the loss function may be the sum of two terms, where the first term is weighted distance correlation between raw data and activation outputs of a split layer of the network, and the second term is weighted categorical cross entropy of actual labels and label predictions. Distance correlation with the entire raw data may be minimized. Alternatively, distance correlation with only with certain features of the raw data may be minimized, in order to ensure attribute-level privacy. In some cases, a client computer calculates decorrelated representations of raw data before sharing information about the data with external computers.

Methods and apparatus for reducing leakage in distributed deep learning

A time-of-flight imaging system may output light with a modulation frequency in the gigahertz band, to illuminate a range target. This high-frequency illumination may enable extremely precise&#x2014;e.g., micron-scale&#x2014;depth measurements. The system may modulate reflected light from the range target, to create a beat tone that has a frequency in the hertz band. In some cases, the modulated light in the gigahertz band is created by a first modulator and the beat tone in the hertz band is created by a second modulator. In some cases, the modulated light in the gigahertz band is created by an upshift cascade of modulators and the beat tone in the hertz band is created by a downshift cascade of modulators. A photodetector may measure the low-frequency beat tone. From this beat tone, phase of the signal and depth of the range target may be extracted.

Methods and apparatus for gigahertz time-of-flight imaging

In one embodiment, a method includes receiving data associated with the user from a content rendering device associated with a user of an online social network, where the content rendering device receives signals from a broadcast service provider system, identifying personalized content items that are of interest to the user based at least on the received data associated with the user, sending the identified personalized content items to the content rendering device, where the identified personalized content items are used by the content rendering device to determine which content items to cache, ranking content objects in the online social network based at least on the identified personalized content items, sending one or more content objects that have higher ranks than the other content objects to the broadcast service provider system, where the one or more content objects are sent to the content rendering device by the broadcast service provider system.

Broadcasting system with integrated social networking

A sample may be illuminated in such a way that light passes through the sample, reflects from a set of reflectors, passes through the sample again and travels to a light sensor. The reflectors may be staggered in depth beneath the sample, each reflector being at a different depth. Light reflecting from each reflector, respectively, may arrive at the light sensor during a different time interval than that in which light reflecting from other reflectors arrives&#x2014;or may have a different phase than that of light reflecting from the other reflectors. The light sensor may separately measure light reflecting from each reflector, respectively. The reflectors may be extremely small, and the separate reflections from the different reflectors may be combined in a super-resolved image. The super-resolved image may have a spatial resolution that is better than that indicated by the diffraction limit.

Methods and apparatus for high resolution imaging with reflectors at staggered depths beneath sample

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Cambridge, MA, United States of America

Ramesh Raskar