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)
WSDM2022 "A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction"

BiRTE WSDM2022 "A Simple but Effective Bidirectional Extraction Framework for Relational Triple Extraction" Requirements The main requirements are: py

9 Dec 27, 2022
Code for ICDM2020 full paper: "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"

Subg-Con Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning (Jiao et al., ICDM 2020): https://arxiv.org/abs/2009.10273 Over

34 Jul 06, 2022
Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.

Population-Based Bandits (PB2) Code for the Population-Based Bandits (PB2) Algorithm, from the paper Provably Efficient Online Hyperparameter Optimiza

Jack Parker-Holder 22 Nov 16, 2022
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

dddzg 49 Jan 02, 2023
1st place solution in CCF BDCI 2021 ULSEG challenge

1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (

Chenxu Peng 30 Nov 22, 2022
This implementation contains the application of GPlearn's symbolic transformer on a commodity futures sector of the financial market.

GPlearn_finiance_stock_futures_extension This implementation contains the application of GPlearn's symbolic transformer on a commodity futures sector

Chengwei <a href=[email protected]"> 189 Dec 25, 2022
ICLR2021 (Under Review)

Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning This repository contains the official PyTorch implementation o

Haoyi Fan 58 Dec 30, 2022
A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

Visdom A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Python. Overview Concepts Setup Usage API To

FOSSASIA 9.4k Jan 07, 2023
PyTorch implementation of Constrained Policy Optimization

PyTorch implementation of Constrained Policy Optimization (CPO) This repository has a simple to understand and use implementation of CPO in PyTorch. A

Sapana Chaudhary 25 Dec 08, 2022
FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics

FusionNet_Pytorch FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics Requirements Pytorch 0.1.11 Pyt

Choi Gunho 102 Dec 13, 2022
Gesture-controlled Video Game. Just swing your finger and play the game without touching your PC

Gesture Controlled Video Game Detailed Blog : https://www.analyticsvidhya.com/blog/2021/06/gesture-controlled-video-game/ Introduction This project is

Devbrat Anuragi 35 Jan 06, 2023
Depth-Aware Video Frame Interpolation (CVPR 2019)

DAIN (Depth-Aware Video Frame Interpolation) Project | Paper Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang IEEE C

Wenbo Bao 7.7k Dec 31, 2022
Deep Learning tutorials in jupyter notebooks.

DeepSchool.io Sign up here for Udemy Course on Machine Learning (Use code DEEPSCHOOL-MARCH to get 85% off course). Goals Make Deep Learning easier (mi

Sachin Abeywardana 1.8k Dec 28, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation [Paper] [PyTorch] [MXNet] [Video] This repository provides code for training

Visual Understanding Lab @ Samsung AI Center Moscow 516 Dec 21, 2022
Cossim - Sharpened Cosine Distance implementation in PyTorch

Sharpened Cosine Distance PyTorch implementation of the Sharpened Cosine Distanc

Istvan Fehervari 10 Mar 22, 2022
DiffStride: Learning strides in convolutional neural networks

DiffStride is a pooling layer with learnable strides. Unlike strided convolutions, average pooling or max-pooling that require cross-validating stride values at each layer, DiffStride can be initiali

Google Research 113 Dec 13, 2022
Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

Point-BERT: Pre-Training 3D Point Cloud Transformers with Masked Point Modeling Created by Xumin Yu*, Lulu Tang*, Yongming Rao*, Tiejun Huang, Jie Zho

Lulu Tang 306 Jan 06, 2023
ADOP: Approximate Differentiable One-Pixel Point Rendering

ADOP: Approximate Differentiable One-Pixel Point Rendering Abstract: We present a novel point-based, differentiable neural rendering pipeline for scen

Darius Rückert 1.9k Jan 06, 2023
Viperdb - A tiny log-structured key-value database written in pure Python

ViperDB 🐍 ViperDB is a lightweight embedded key-value store written in pure Pyt

17 Oct 17, 2022