Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)

Related tags

Deep LearningNLOS-OT
Overview

NLOS-OT

Official implementation of NLOS-OT: Passive Non-Line-of-Sight Imaging Using Optimal Transport (IEEE TIP, accepted)

Description

In this repository, we release the NLOS-OT code in Pytorch as well as the passive NLOS imaging dataset NLOS-Passive.

  • Problem statement: Passive NLOS imaging

  • NLOS-OT architecture

  • The reconstruction results of NLOS-OT trained by specific dataset without partial occluder

  • The generalization results of NLOS-OT trained by dataset only from STL-10 with unknown partial occluder

Installation

  1. install required packages

  2. clone the repo

Prepare Data

  1. Download dataset

You can download each group in NLOS-Passive through the link below. Please note that a compressed package (.zip or .z01+.zip) represents a group of measured data.

link:https://pan.baidu.com/s/19Q48BWm1aJQhIt6BF9z-uQ

code:j3p2

If the link fails, please feel free to contact me.

  1. Organize the files structure of the dataset

Demo / Evaluate

Before that, you should have installed the required packages and organized the data set according to the appropriate file structure.

  1. Download pretrained pth

  2. run the test.py

Train

Before that, you should have installed the required packages and organized the data set according to the appropriate file structure.

Citation

If you find our work and code helpful, please consider cite:

We thank the following great works:

  • DeblurGAN, pix2pix: Our code is based on the framework provided by the two repos.

  • IntroVAE: The encoder and decoder in NLOS-OT are based on IntroVAE.

  • AE-OT, AEOT-GAN: The idea of using OT to complete passive NLOS imaging tasks in NLOS-OT comes from the two works.

If you find them helpful, please cite:

@inproceedings{kupynDeblurGANBlindMotion2018,
	title = {{DeblurGAN}: {Blind} {Motion} {Deblurring} {Using} {Conditional} {Adversarial} {Networks}},
	booktitle = {2018 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	author = {Kupyn, Orest and Budzan, Volodymyr and Mykhailych, Mykola and Mishkin, Dmytro and Matas, Jiri},
	year = {2018},
}

@inproceedings{isolaImagetoimageTranslationConditional2017,
	title = {Image-to-image translation with conditional adversarial networks},
	booktitle = {2017 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A.},
	year = {2017},
	pages = {5967--5976},
}

@inproceedings{huang_introvae_2018,
	title = {{IntroVAE}: {Introspective} {Variational} {Autoencoders} for {Photographic} {Image} {Synthesis}},
	shorttitle = {{IntroVAE}},
	urldate = {2020-07-14},
	booktitle = {Advances in neural information processing systems},
	author = {Huang, Huaibo and Li, Zhihang and He, Ran and Sun, Zhenan and Tan, Tieniu},
	month = oct,
	year = {2018}
}

@article{an_ae-ot-gan_2020,
	title = {{AE}-{OT}-{GAN}: {Training} {Gans} from {Data} {Specific} {Latent} {Distribution}},
	shorttitle = {Ae-{Ot}-{Gan}},
	journal = {arXiv},
	author = {An, Dongsheng and Guo, Yang and Zhang, Min and Qi, Xin and Lei, Na and Yau, Shing-Tung and Gu, Xianfeng},
	year = {2020}
}

@inproceedings{an_ae-ot_2020,
	title = {{AE}-{OT}: {A} {NEW} {GENERATIVE} {MODEL} {BASED} {ON} {EX}- {TENDED} {SEMI}-{DISCRETE} {OPTIMAL} {TRANSPORT}},
	language = {en},
	author = {An, Dongsheng and Guo, Yang and Lei, Na and Luo, Zhongxuan and Yau, Shing-Tung and Gu, Xianfeng},
	year = {2020},
	pages = {19},
}
Owner
Ruixu Geng(耿瑞旭)
Undergraduate 2015 - 2019 (Expected), Information and Communication Engineering, UESTC
Ruixu Geng(耿瑞旭)
PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML)

pytorch-maml This is a PyTorch implementation of the supervised learning experiments from the paper Model-Agnostic Meta-Learning (MAML): https://arxiv

Kate Rakelly 516 Jan 05, 2023
Transfer Learning Remote Sensing

Transfer_Learning_Remote_Sensing Simulation R codes for data generation and visualizations are in the folder simulation. Experiment: California Housin

2 Jun 21, 2022
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022)

CMUA-Watermark The official code for CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes (AAAI2022) arxiv. It is bas

50 Nov 26, 2022
A fast model to compute optical flow between two input images.

DCVNet: Dilated Cost Volumes for Fast Optical Flow This repository contains our implementation of the paper: @InProceedings{jiang2021dcvnet, title={

Huaizu Jiang 8 Sep 27, 2021
Distributional Sliced-Wasserstein distance code

Distributional Sliced Wasserstein distance This is a pytorch implementation of the paper "Distributional Sliced-Wasserstein and Applications to Genera

VinAI Research 39 Jan 01, 2023
Face Recognition & AI Based Smart Attendance Monitoring System.

In today’s generation, authentication is one of the biggest problems in our society. So, one of the most known techniques used for authentication is h

Sagar Saha 1 Jan 14, 2022
Applying curriculum to meta-learning for few shot classification

Curriculum Meta-Learning for Few-shot Classification We propose an adaptation of the curriculum training framework, applicable to state-of-the-art met

Stergiadis Manos 3 Oct 25, 2022
DABO: Data Augmentation with Bilevel Optimization

DABO: Data Augmentation with Bilevel Optimization [Paper] The goal is to automatically learn an efficient data augmentation regime for image classific

ElementAI 24 Aug 12, 2022
HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty

HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty Giorgio Cantarini, Francesca Odone, Nicoletta Noceti, Federi

18 Aug 02, 2022
3D Human Pose Machines with Self-supervised Learning

3D Human Pose Machines with Self-supervised Learning Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei, “3D Human Pose Machines with Self

Chenhan Jiang 398 Dec 20, 2022
Progressive Domain Adaptation for Object Detection

Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-fa

96 Nov 25, 2022
Lab Materials for MIT 6.S191: Introduction to Deep Learning

This repository contains all of the code and software labs for MIT 6.S191: Introduction to Deep Learning! All lecture slides and videos are available

Alexander Amini 5.6k Dec 26, 2022
Machine learning library for fast and efficient Gaussian mixture models

This repository contains code which implements the Stochastic Gaussian Mixture Model (S-GMM) for event-based datasets Dependencies CMake Premake4 Blaz

Omar Oubari 1 Dec 19, 2022
Single-Stage 6D Object Pose Estimation, CVPR 2020

Overview This repository contains the code for the paper Single-Stage 6D Object Pose Estimation. Yinlin Hu, Pascal Fua, Wei Wang and Mathieu Salzmann.

CVLAB @ EPFL 89 Dec 26, 2022
Fine-grained Control of Image Caption Generation with Abstract Scene Graphs

Faster R-CNN pretrained on VisualGenome This repository modifies maskrcnn-benchmark for object detection and attribute prediction on VisualGenome data

Shizhe Chen 7 Apr 20, 2021
Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

TF Watcher TF Watcher is a simple to use Python package and web app which allows you to monitor 👀 your Machine Learning training or testing process o

Rishit Dagli 54 Nov 01, 2022
Wileless-PDGNet Implementation

Wileless-PDGNet Implementation This repo is related to the following paper: Boning Li, Ananthram Swami, and Santiago Segarra, "Power allocation for wi

6 Oct 04, 2022
Code for database and frontend of webpage for Neural Fields in Visual Computing and Beyond.

Neural Fields in Visual Computing—Complementary Webpage This is based on the amazing MiniConf project from Hendrik Strobelt and Sasha Rush—thank you!

Brown University Visual Computing Group 29 Nov 30, 2022
A python/pytorch utility library

A python/pytorch utility library

Jiaqi Gu 5 Dec 02, 2022
Point-NeRF: Point-based Neural Radiance Fields

Point-NeRF: Point-based Neural Radiance Fields Project Sites | Paper | Primary c

Qiangeng Xu 662 Jan 01, 2023