Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020

Overview

Official respository for "Modeling Defocus-Disparity in Dual-Pixel Sensors", ICCP 2020

BibTeX

@INPROCEEDINGS{punnappurath2020modeling,
   author={Abhijith Punnappurath and Abdullah Abuolaim and Mahmoud Afifi and Michael S. Brown},
   booktitle={IEEE International Conference on Computational Photography (ICCP)}, 
   title={Modeling Defocus-Disparity in Dual-Pixel Sensors}, 
   year={2020}
}

How to run

  • This is the code for the optimization-based approach described in Section 4.1 of our paper. The implementation is in Matlab.
  • The post-process edge-aware filtering described in Section 4.3 consists of a bilateral solver and a guided filter. Official implementations released by the authors have been used - the bilateral solver is in Python and the guided filter in Matlab.
  • To obtain our final result, run Steps 1, 2, and 3 sequentially, where Step 1 is the main optimization, and Steps 2 and 3 are the post-process bilateral solver and guided filter, respectively.
  • The data corresponding to Fig. 7 of our paper can be found here, and Figs. 1 and 8 can be found here.
  • Running the code as is produces our result in Fig. 8(f) third column.
  • Other outputs can be generated by appropriately setting the input image path here in Step 1, and here and here in Steps 2 and 3, respectively.
    • The img_name variable to use for Steps 2 and 3 will be displayed when Step 1 finishes execution.
  • Note that the optimization-based approach is very slow since it requires minimizing our cost function of equation (7) at each window.
  • Evaluation code can be found here

Dataset

  • Download our dataset used for evaluation in Section 5.4 here.
    • All results in Table 1 were reported on 16-bit uncompressed TIFF images. The dataset shared on the link above contains 8-bit JPEG images (for file size considerations). Results may vary slightly.

Video spotlight

  • Watch the YouTube video here

Visualization of our parameterized dual-pixel kernel of equation 6

DP gif

Also check out

You might also like...
The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.
The official implementation of Equalization Loss v1 & v2 (CVPR 2020, 2021) based on MMDetection.

The Equalization Losses for Long-tailed Object Detection and Instance Segmentation This repo is official implementation CVPR 2021 paper: Equalization

Official PyTorch code for CVPR 2020 paper
Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision https://arxiv.org/abs/2003.00393 Abstract Active learning (AL) aims to min

[NeurIPS 2020] Official repository for the project
[NeurIPS 2020] Official repository for the project "Listening to Sound of Silence for Speech Denoising"

Listening to Sounds of Silence for Speech Denoising Introduction This is the repository of the "Listening to Sounds of Silence for Speech Denoising" p

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

PyTorch implementation of
PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020)

PyTorch implementation of Conformer: Convolution-augmented Transformer for Speech Recognition. Transformer models are good at capturing content-based

Code for ACM MM 2020 paper
Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination The offical implementation for the "NOH-NMS: Improving Pedestrian Detection by

[ECCV 2020] Reimplementation of 3DDFAv2, including face mesh, head pose, landmarks, and more.
[ECCV 2020] Reimplementation of 3DDFAv2, including face mesh, head pose, landmarks, and more.

Stable Head Pose Estimation and Landmark Regression via 3D Dense Face Reconstruction Reimplementation of (ECCV 2020) Towards Fast, Accurate and Stable

MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。
MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

mediapipe-python-sample MediaPipeのPythonパッケージのサンプルです。 2020/12/11時点でPython実装のある以下4機能について用意しています。 Hands Pose Face Mesh Holistic Requirement mediapipe 0.

Comments
  • How could I get the left and right images?

    How could I get the left and right images?

    Hi. Thank you for your inspired work. I have a question. How could I get the left and right images(RGB) with raw dual-pixel data? Looking forward to your reply.

    opened by Courageux-J 2
  • Please provide the implementation for CNN approach

    Please provide the implementation for CNN approach

    Hi, Thank you for your inspired work!

    I am interested in your CNN approach to estimate depth. Could you please provide the implementation, as well as checkpoint of your pre-trained model?

    Thank you and look foward to you answer!

    BR Hiep

    opened by truongconghiep 1
Owner
Abhijith Punnappurath
Abhijith Punnappurath
UDP++ (ECCVW 2020 Oral), (Winner of COCO 2020 Keypoint Challenge).

UDP-Pose This is the pytorch implementation for UDP++, which won the Fisrt place in COCO Keypoint Challenge at ECCV 2020 Workshop. Top-Down Results on

null 20 Jul 29, 2022
An official implementation of "SFNet: Learning Object-aware Semantic Correspondence" (CVPR 2019, TPAMI 2020) in PyTorch.

PyTorch implementation of SFNet This is the implementation of the paper "SFNet: Learning Object-aware Semantic Correspondence". For more information,

CV Lab @ Yonsei University 87 Dec 30, 2022
Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

null 46 Nov 9, 2022
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
Official Pytorch implementation of 'GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network' (NeurIPS 2020)

Official implementation of GOCor This is the official implementation of our paper : GOCor: Bringing Globally Optimized Correspondence Volumes into You

Prune Truong 71 Nov 18, 2022
Official Implementation of Swapping Autoencoder for Deep Image Manipulation (NeurIPS 2020)

Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang UC

null 449 Dec 27, 2022
Official implementation of "Accelerating Reinforcement Learning with Learned Skill Priors", Pertsch et al., CoRL 2020

Accelerating Reinforcement Learning with Learned Skill Priors [Project Website] [Paper] Karl Pertsch1, Youngwoon Lee1, Joseph Lim1 1CLVR Lab, Universi

Cognitive Learning for Vision and Robotics (CLVR) lab @ USC 134 Dec 6, 2022
Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)

End-to-End Optimization of Scene Layout Code release for: End-to-End Optimization of Scene Layout CVPR 2020 (Oral) Project site, Bibtex For help conta

Andrew Luo 41 Dec 9, 2022
This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

README This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et a

Raghav 42 Dec 15, 2022
The official project of SimSwap (ACM MM 2020)

SimSwap: An Efficient Framework For High Fidelity Face Swapping Proceedings of the 28th ACM International Conference on Multimedia The official reposi

Six_God 2.6k Jan 8, 2023