Search Results for author: Ramesh Raskar

Found 88 papers, 18 papers with code

Imaging Behind Occluders Using Two-Bounce Light

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.

Vocal Bursts Valence Prediction

Towards Viewpoint Robustness in Bird's Eye View Segmentation

no code implementations11 Sep 2023 Tzofi Klinghoffer, Jonah Philion, Wenzheng Chen, Or Litany, Zan Gojcic, Jungseock Joo, Ramesh Raskar, Sanja Fidler, Jose M. Alvarez

We introduce a technique for novel view synthesis and use it to transform collected data to the viewpoint of target rigs, allowing us to train BEV segmentation models for diverse target rigs without any additional data collection or labeling cost.

Autonomous Vehicles Novel View Synthesis

Conformal Prediction with Large Language Models for Multi-Choice Question Answering

1 code implementation28 May 2023 Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam

In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering.

Conformal Prediction Multiple-choice +1

Federated Conformal Predictors for Distributed Uncertainty Quantification

1 code implementation27 May 2023 Charles Lu, Yaodong Yu, Sai Praneeth Karimireddy, Michael I. Jordan, Ramesh Raskar

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models.

Conformal Prediction Federated Learning

Domain Generalization In Robust Invariant Representation

1 code implementation7 Apr 2023 Gauri Gupta, Ritvik Kapila, Keshav Gupta, Ramesh Raskar

Unsupervised approaches for learning representations invariant to common transformations are used quite often for object recognition.

Domain Generalization Object Recognition

Role of Transients in Two-Bounce Non-Line-of-Sight Imaging

no code implementations CVPR 2023 Siddharth Somasundaram, Akshat Dave, Connor Henley, Ashok Veeraraghavan, Ramesh Raskar

Specifically, we study how ToF information can reduce the number of measurements and spatial resolution needed for shape reconstruction.

Vocal Bursts Valence Prediction

ORCa: Glossy Objects As Radiance-Field Cameras

no code implementations CVPR 2023 Kushagra Tiwary, Akshat Dave, Nikhil Behari, Tzofi Klinghoffer, Ashok Veeraraghavan, Ramesh Raskar

By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.

Novel View Synthesis

ORCa: Glossy Objects as Radiance Field Cameras

1 code implementation8 Dec 2022 Kushagra Tiwary, Akshat Dave, Nikhil Behari, Tzofi Klinghoffer, Ashok Veeraraghavan, Ramesh Raskar

By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e. g. from reflections on the human eye.

Novel View Synthesis

Scalable Collaborative Learning via Representation Sharing

no code implementations20 Nov 2022 Frédéric Berdoz, Abhishek Singh, Martin Jaggi, Ramesh Raskar

To do so, each client releases averaged last hidden layer activations of similar labels to a central server that only acts as a relay (i. e., is not involved in the training or aggregation of the models).

Federated Learning Knowledge Distillation +1

Differentially Private CutMix for Split Learning with Vision Transformer

no code implementations28 Oct 2022 Seungeun Oh, Jihong Park, Sihun Baek, Hyelin Nam, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim

Split learning (SL) detours this by communicating smashed data at a cut-layer, yet suffers from data privacy leakage and large communication costs caused by high similarity between ViT' s smashed data and input data.

Federated Learning Privacy Preserving

Detection and Mapping of Specular Surfaces Using Multibounce Lidar Returns

no code implementations7 Sep 2022 Connor Henley, Siddharth Somasundaram, Joseph Hollmann, Ramesh Raskar

We propose methods that use specular, multibounce lidar returns to detect and map specular surfaces that might be invisible to conventional lidar systems that rely on direct, single-scatter returns.

Fundamentals of Task-Agnostic Data Valuation

no code implementations25 Aug 2022 Mohammad Mohammadi Amiri, Frederic Berdoz, Ramesh Raskar

We capture these statistical differences through second moment by measuring diversity and relevance of the seller's data for the buyer; we estimate these measures through queries to the seller without requesting raw data.

Data Valuation

Differentiable Agent-based Epidemiology

1 code implementation20 Jul 2022 Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Arnau Quera-Bofarull, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar

Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments.

Epidemiology Navigate

Private independence testing across two parties

no code implementations8 Jul 2022 Praneeth Vepakomma, Mohammad Mohammadi Amiri, Clément L. Canonne, Ramesh Raskar, Alex Pentland

We introduce $\pi$-test, a privacy-preserving algorithm for testing statistical independence between data distributed across multiple parties.

Privacy Preserving Vocal Bursts Valence Prediction

Physically Disentangled Representations

1 code implementation11 Apr 2022 Tzofi Klinghoffer, Kushagra Tiwary, Arkadiusz Balata, Vivek Sharma, Ramesh Raskar

In this paper, we show the utility of inverse rendering in learning representations that yield improved accuracy on downstream clustering, linear classification, and segmentation tasks with the help of our novel Leave-One-Out, Cycle Contrastive loss (LOOCC), which improves disentanglement of scene parameters and robustness to out-of-distribution lighting and viewpoints.

Classification Disentanglement +2

Towards Learning Neural Representations from Shadows

no code implementations29 Mar 2022 Kushagra Tiwary, Tzofi Klinghoffer, Ramesh Raskar

We observe that shadows are a powerful cue that can constrain neural scene representations to learn SfS, and even outperform NeRF to reconstruct otherwise hidden geometry.

3D Reconstruction Neural Rendering

Learning to Censor by Noisy Sampling

no code implementations23 Mar 2022 Ayush Chopra, Abhinav Java, Abhishek Singh, Vivek Sharma, Ramesh Raskar

The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks.

Decouple-and-Sample: Protecting sensitive information in task agnostic data release

no code implementations17 Mar 2022 Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar

While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns.

AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning

no code implementations2 Dec 2021 Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar

In this work, we introduce AdaSplit which enables efficiently scaling SL to low resource scenarios by reducing bandwidth consumption and improving performance across heterogeneous clients.

Federated Learning

Private measurement of nonlinear correlations between data hosted across multiple parties

no code implementations19 Oct 2021 Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar

We introduce a differentially private method to measure nonlinear correlations between sensitive data hosted across two entities.

Causal Inference

DeepABM: Scalable, efficient and differentiable agent-based simulations via graph neural networks

no code implementations9 Oct 2021 Ayush Chopra, Esma Gel, Jayakumar Subramanian, Balaji Krishnamurthy, Santiago Romero-Brufau, Kalyan S. Pasupathy, Thomas C. Kingsley, Ramesh Raskar

We introduce DeepABM, a framework for agent-based modeling that leverages geometric message passing of graph neural networks for simulating action and interactions over large agent populations.

Sanitizer: Sanitizing data for anonymizing sensitive information

no code implementations29 Sep 2021 Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar

This is done in a two-step process: first, we develop a method that encodes unstructured image-like modality into a structured representation bifurcated by sensitive and non-sensitive representation.

Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity

no code implementations19 Aug 2021 Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar

We provide a parallel algorithm with a time complexity over $n$ processors of $\mathcal{O}(n^2g) +\mathcal{O}(\log{\log{n}})$ where $n$ is the cardinality of the ground set and $g$ is the complexity to compute the monotone linkage function that induces a corresponding quasi-concave set function via a duality.

Combinatorial Optimization

Automatic calibration of time of flight based non-line-of-sight reconstruction

no code implementations21 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.

Can Self Reported Symptoms Predict Daily COVID-19 Cases?

1 code implementation18 May 2021 Parth Patwa, Viswanatha Reddy, Rohan Sukumaran, Sethuraman TV, Eptehal Nashnoush, Sheshank Shankar, Rishemjit Kaur, Abhishek Singh, Ramesh Raskar

The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states.

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

no code implementations2 May 2021 Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar

In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples.

BIG-bench Machine Learning Data Augmentation +1

PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval

no code implementations22 Feb 2021 Praneeth Vepakomma, Julia Balla, Ramesh Raskar

1) We present a novel differentially private method \textit{PrivateMail} for supervised manifold learning, the first of its kind to our knowledge.

Content-Based Image Retrieval Retrieval

Objects As Cameras: Estimating High-Frequency Illumination From Shadows

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.

Vocal Bursts Intensity Prediction

COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

no code implementations21 Dec 2020 Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, Ramesh Raskar

In this study, we understand trends in the spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms surveys as an alternative to COVID-19 testing reports.

Time Series Forecasting

Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic

no code implementations26 Sep 2020 Mikhail Dmitrienko, Abhishek Singh, Patrick Erichsen, Ramesh Raskar

In this work we propose a WiFi colocation methodology for digital contact tracing.

Computers and Society Signal Processing

NoPeek: Information leakage reduction to share activations in distributed deep learning

1 code implementation20 Aug 2020 Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar

For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy.

SplitNN-driven Vertical Partitioning

no code implementations7 Aug 2020 Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar

In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features.

Splintering with distributions: A stochastic decoy scheme for private computation

no code implementations6 Jul 2020 Praneeth Vepakomma, Julia Balla, Ramesh Raskar

Performing computations while maintaining privacy is an important problem in todays distributed machine learning solutions.

Automatic Differentiation for All Photons Imaging to See Inside Volumetric Scattering Media

no code implementations2 Jun 2020 Tomohiro Maeda, Ankit Ranjan, Ramesh Raskar

Imaging through dense scattering media - such as biological tissue, fog, and smoke - has applications in the medical and robotics fields.

Privacy Guidelines for Contact Tracing Applications

no code implementations28 Apr 2020 Manish Shukla, Rajan M A, Sachin Lodha, Gautam Shroff, Ramesh Raskar

Due to this there is an emergence of mobile based applications for contact tracing.

Privacy in Deep Learning: A Survey

no code implementations25 Apr 2020 Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh

In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.

Recommendation Systems

Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic

1 code implementation19 Mar 2020 Ramesh Raskar, Isabel Schunemann, Rachel Barbar, Kristen Vilcans, Jim Gray, Praneeth Vepakomma, Suraj Kapa, Andrea Nuzzo, Rajiv Gupta, Alex Berke, Dazza Greenwood, Christian Keegan, Shriank Kanaparti, Robson Beaudry, David Stansbury, Beatriz Botero Arcila, Rishank Kanaparti, Francesco M Benedetti, Alina Clough, Riddhiman Das, Kaushal Jain, Khahlil Louisy, Greg Nadeau, Vitor Pamplona, Steve Penrod, Yasaman Rajaee, Abhishek Singh, Greg Storm, John Werner

Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited.

Cryptography and Security Computers and Society Distributed, Parallel, and Cluster Computing

Split Learning for collaborative deep learning in healthcare

no code implementations27 Dec 2019 Maarten G. Poirot, Praneeth Vepakomma, Ken Chang, Jayashree Kalpathy-Cramer, Rajiv Gupta, Ramesh Raskar

Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up.

Binary Classification Multi-Label Classification

Recent Advances in Imaging Around Corners

no code implementations12 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.

ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations

no code implementations9 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).

Model Selection

Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest

no code implementations27 Sep 2019 Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar

In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection.

Data Poisoning

Detailed comparison of communication efficiency of split learning and federated learning

no code implementations18 Sep 2019 Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar

We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning.

Federated Learning

Data Markets to support AI for All: Pricing, Valuation and Governance

no code implementations14 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.

Light-Field for RF

no code implementations13 Jan 2019 Manikanta Kotaru, Guy Satat, Ramesh Raskar, Sachin Katti

In the context of imaging, RF spectrum holds many advantages compared to visible light systems.

Privacy Preserving RF-based Pose Estimation

No Peek: A Survey of private distributed deep learning

no code implementations8 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.

Federated Learning

A Review of Homomorphic Encryption Libraries for Secure Computation

1 code implementation6 Dec 2018 Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar, Ranjan Ramachandra, Santanu Bhattacharya

In this paper we provide a survey of various libraries for homomorphic encryption.

Cryptography and Security

Maximum-Entropy Fine Grained Classification

no code implementations NeurIPS 2018 Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik

Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.

Ranked #16 on Fine-Grained Image Classification on NABirds (using extra training data)

Classification Fine-Grained Image Classification +1

Flash Photography for Data-Driven Hidden Scene Recovery

no code implementations27 Oct 2018 Matthew Tancik, Guy Satat, Ramesh Raskar

The method is able to localize 12cm wide hidden objects in 2D with 1. 7cm accuracy.

Object Localization

Distributed learning of deep neural network over multiple agents

no code implementations14 Oct 2018 Otkrist Gupta, Ramesh Raskar

Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.

Maximum-Entropy Fine-Grained Classification

no code implementations16 Sep 2018 Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik

Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.

Classification Fine-Grained Image Classification +1

DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

1 code implementation17 May 2018 Ilke Demir, Krzysztof Koperski, David Lindenbaum, Guan Pang, Jing Huang, Saikat Basu, Forest Hughes, Devis Tuia, Ramesh Raskar

We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images.

Learning Gaze Transitions From Depth to Improve Video Saliency Estimation

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.

Saliency Prediction

Accelerating Neural Architecture Search using Performance Prediction

2 code implementations ICLR 2018 Bowen Baker, Otkrist Gupta, Ramesh Raskar, Nikhil Naik

Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations.

Hyperparameter Optimization Language Modelling +3

Sampling Without Time: Recovering Echoes of Light via Temporal Phase Retrieval

no code implementations27 Jan 2017 Ayush Bhandari, Aurelien Bourquard, Ramesh Raskar

This topic has its roots in the problem of recovering multiple echoes of light from its low-pass filtered and auto-correlated, time-domain measurements.


Designing Neural Network Architectures using Reinforcement Learning

5 code implementations7 Nov 2016 Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar

We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task.

General Classification Image Classification +3

Lensless Imaging with Compressive Ultrafast Sensing

no code implementations19 Oct 2016 Guy Satat, Matthew Tancik, Ramesh Raskar

Each sensor acquisition is encoded with a different illumination pattern and produces a time series where time is a function of the photon's origin in the scene.

Compressive Sensing Time Series +1

Deep Learning the City : Quantifying Urban Perception At A Global Scale

no code implementations5 Aug 2016 Abhimanyu Dubey, Nikhil Naik, Devi Parikh, Ramesh Raskar, César A. Hidalgo

Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavior and health of its residents.

General Classification

Shape from Mixed Polarization

no code implementations6 May 2016 Vage Taamazyan, Achuta Kadambi, Ramesh Raskar

In this paper, we propose a new method that jointly uses viewpoint and polarization data to holistically separate diffuse and specular components, recover refractive index, and ultimately recover 3D shape.

Sweep Distortion Removal from THz Images via Blind Demodulation

no code implementations29 Mar 2016 Alireza Aghasi, Barmak Heshmat, Albert Redo-Sanchez, Justin Romberg, Ramesh Raskar

Heavy sweep distortion induced by alignments and inter-reflections of layers of a sample is a major burden in recovering 2D and 3D information in time resolved spectral imaging.


Multi-velocity neural networks for gesture recognition in videos

no code implementations22 Mar 2016 Otkrist Gupta, Dan Raviv, Ramesh Raskar

We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification.

Action Recognition General Classification +2

Deep video gesture recognition using illumination invariants

no code implementations21 Mar 2016 Otkrist Gupta, Dan Raviv, Ramesh Raskar

In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling.

Gesture Recognition

Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution

no code implementations ICCV 2015 W. Williem, Ramesh Raskar, In Kyu Park

In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images.

Colorization Stereo Matching +1

Coreset-Based Adaptive Tracking

no code implementations19 Nov 2015 Abhimanyu Dubey, Nikhil Naik, Dan Raviv, Rahul Sukthankar, Ramesh Raskar

We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment.

Object Tracking

A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors

no code implementations CVPR 2015 Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.

A Light Transport Model for Mitigating Multipath Interference in TOF Sensors

no code implementations CVPR 2015 Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.

Discovering the Structure of a Planar Mirror System from Multiple Observations of a Single Point

no code implementations CVPR 2013 Ilya Reshetouski, Alkhazur Manakov, Ayush Bandhari, Ramesh Raskar, Hans-Peter Seidel, Ivo Ihrke

We investigate the problem of identifying the position of a viewer inside a room of planar mirrors with unknown geometry in conjunction with the room's shape parameters.

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