Transient Imaging Data

Transient imaging data analysis focuses on extracting meaningful information from time-dependent light signals, aiming to reconstruct scenes or understand dynamic processes from these measurements. Current research emphasizes the use of deep learning models, including autoencoders, recurrent neural networks, and Bayesian neural networks, often coupled with dimensionality reduction techniques like functional principal component analysis, to process this complex data and address challenges like multi-path interference in time-of-flight imaging. These advancements are improving the accuracy and efficiency of non-line-of-sight imaging, enabling better understanding of complex systems in various fields, from industrial condition monitoring to IoT network optimization.

Papers

March 28, 2024

NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data
Matteo Caligiuri, Adriano Simonetto, Pietro Zanuttigh
Deep Learning Model Synthetic Dataset Flight Data Non Line of Sight Imaging Flight Sensor Transient Imaging Data

July 10, 2023

Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data
Ziyu Xie, Mahmoud Yaseen, Xu Wu
Deeper Network Bayesian Uncertainty Functional Principal Component Transient Imaging Data Clad Characteristic Inverse UQ

September 9, 2022

Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
Jonas Köhne, Lars Henning, Clemens Gühmann
Recurrent Neural Network Multivariate Time Series Data Time Series Clustering Iterative Neural Network Time Series Subsequence Transient Imaging Data

March 16, 2022

A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data
Hongda Wu, Ali Nasehzadeh, Ping Wang
Deep Reinforcement Learning Internet of Thing Deep Reinforcement Internet of Thing Network Cache Context Internet of Thing Data Caching Strategy Transient Imaging Data

November 29, 2021

Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction
Adriano Simonetto, Gianluca Agresti, Pietro Zanuttigh, Henrik Schäfer
Neural Network Depth Image Dynamic Reconstruction Multipath Propagation Transient Imaging Data iToF Depth

Transient Imaging Data

Papers

NIGHT -- Non-Line-of-Sight Imaging from Indirect Time of Flight Data

Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data

Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data

Lightweight Deep Learning Architecture for MPI Correction and Transient Reconstruction