Source code for "OmniPhotos: Casual 360° VR Photography"

Overview

OmniPhotos: Casual 360° VR Photography

Project Page | Video | Paper | Demo | Data

This repository contains the source code for creating and viewing OmniPhotos – a new approach for casual 360° VR photography using a consumer 360° video camera.

OmniPhotos: Casual 360° VR Photography
Tobias Bertel, Mingze Yuan, Reuben Lindroos, Christian Richardt
ACM Transactions on Graphics (SIGGRAPH Asia 2020)

Demo

The quickest way to try out OmniPhotos is via our precompiled demo (610 MB). Download and unzip to get started. Documentation for the precompiled binaries, which can also be downloaded separately (25 MB), can be found in the downloaded demo directory.

For the demo to run smoothly, we recommend a recently updated Windows 10 machine with a discrete GPU.

Additional OmniPhotos

We provide 31 OmniPhotos for download:

  • 9 preprocessed datasets that are ready for viewing (3.2 GB zipped, 12.8 GB uncompressed)
  • 31 unprocessed datasets with their input videos, camera poses etc.; this includes the 9 preprocessed datasets (17.4 GB zipped, 17.9 GB uncompressed)

Note: A few of the .insv files are missing for the 5.7k datasets. If you need to process these from scratch (using the insv files) these files can be found here.

How to view OmniPhotos

OmniPhotos are viewed using the "Viewer" executable, either in windowed mode (default) or in a compatible VR headset (see below). To run the viewer executable on the preprocessed datasets above, run the command:

Viewer.exe path-to-datasets/Preprocessed/

with paths adjusted for your machine. The viewer will automatically load the first dataset in the directory (in alphabetical order) and give you the option to load any of the datasets in the directory.

If you would like to run the viewer with VR enabled, please ensure that the firmware for your HMD is updated, you have SteamVR installed on your machine, and then run the command:

Viewer.exe --vr path-to-datasets/Preprocessed/

The OmniPhotos viewer can also load a specific single dataset directly:

Viewer.exe [--vr] path-to-datasets/Preprocessed/Temple3/Config/config-viewer.yaml

How to preprocess datasets

If you would like to preprocess additional datasets, for example "Ship" in the "Unprocessed" directory, run the command:

Preprocessing.exe path-to-datasets/Unpreprocessed/Ship/Config/config-viewer.yaml

This will preprocess the dataset according to the options specified in the config file. Once the preprocessing is finished, the dataset can be opened in the Viewer.

For processing new datasets from scratch, please follow the detailed documentation at Python/preprocessing/readme.md.

Compiling from source

The OmniPhotos Preprocessing and Viewer applications are written in C++11, with some Python used for preparing datasets.

Both main applications and the included libraries use CMake as build system generator. We recommend CMake 3.16 or newer, but older 3.x versions might also work.

Our code has been developed and tested with Microsoft Visual Studio 2015 and 2019 (both 64 bit).

Required dependencies

  1. GLFW 3.3 (version 3.3.1 works)
  2. Eigen 3.3 (version 3.3.2 works)
    • Please note: Ceres (an optional dependency) requires Eigen version "3.3.90" (~Eigen master branch).
  3. OpenCV 4.2
    • OpenCV 4.2 includes DIS flow in the main distribution, so precompiled OpenCV can be used.
    • OpenCV 4.1.1 needs to be compiled from source with the optflow contrib package (for DIS flow).
    • We also support the CUDA Brox flow from the cudaoptflow module, if it is compiled in. In this case, tick USE_CUDA_IN_OPENCV in CMake.
  4. OpenGL 4.1: provided by the operating system
  5. glog (newer than 0.4.0 master works)
  6. gflags (version 2.2.2 works)

Included dependencies (in /src/3rdParty/)

  1. DearImGui 1.79: included automatically as a git submodule.
  2. GL3W
  3. JsonCpp 1.8.0: almalgamated version
  4. nlohmann/json 3.6.1
  5. OpenVR 1.10.30: enable with WITH_OPENVR in CMake.
  6. TCLAP
  7. tinyfiledialogs 3.3.8

Optional dependencies

  1. Ceres (with SuiteSparse) is required for the scene-adaptive proxy geometry fitting. Enable with USE_CERES in CMake.
  2. googletest (master): automatically added when WITH_TEST is enabled in CMake.

Citation

Please cite our paper if you use this code or any of our datasets:

@article{OmniPhotos,
  author    = {Tobias Bertel and Mingze Yuan and Reuben Lindroos and Christian Richardt},
  title     = {{OmniPhotos}: Casual 360° {VR} Photography},
  journal   = {ACM Transactions on Graphics},
  year      = {2020},
  volume    = {39},
  number    = {6},
  pages     = {266:1--12},
  month     = dec,
  issn      = {0730-0301},
  doi       = {10.1145/3414685.3417770},
  url       = {https://richardt.name/omniphotos/},
}

Acknowledgements

We thank the reviewers for their thorough feedback that has helped to improve our paper. We also thank Peter Hedman, Ana Serrano and Brian Cabral for helpful discussions, and Benjamin Attal for his layered mesh rendering code.

This work was supported by EU Horizon 2020 MSCA grant FIRE (665992), the EPSRC Centre for Doctoral Training in Digital Entertainment (EP/L016540/1), RCUK grant CAMERA (EP/M023281/1), an EPSRC-UKRI Innovation Fellowship (EP/S001050/1), a Rabin Ezra Scholarship and an NVIDIA Corporation GPU Grant.

Comments
  • Documentation pipeline update

    Documentation pipeline update

    Pipeline to automatically create documenation on read the docs using doxygen.

    • [ ] link to github page on index.html (mainpage.hpp)
    • [ ] add documentation convention to docs\README.md
    • [ ] fork branch from cr333/main and apply changes to that
    • [ ] create new PR from forked branch
    opened by reubenlindroos 1
  • Adds a progress bar when circleselector is running

    Adds a progress bar when circleselector is running

    Also improves speed of the circleselector module by ~50%

    Todo:

    • [x] add tqdm to requirements.txt
    • [x] np.diffs for find_path_length
    • [x] atomic lock for incrementing the progress bar?
    opened by reubenlindroos 0
  • Circle Selector

    Circle Selector

    • [x] clean up requirements.txt
    • [x] save plot of heatmaps to the cache/dataset directory.
    • [x] move json file to capture directory
    • [x] update the template with option to switch off circlefitting
    • [x] update template to remove some of the options (e.g op_filename_expression)
    • [x] Update README.md with automatic circle selection (section 2.2)
    • [x] Update documentation for installation?
    • [x] Linting (spacing), comment convention, Pep convention
    • [x] sort imports
    • [x] replace op_filename_epression with original_filename_expression

    cv_utils

    • [x] remove extra copy of computeColor
    • [x] change pjoin to os.path.join
    • [x] add more documentation for parameters in cv_utils (change lookatang to look_at_angle)
    • [x] 'nxt' to 'next'
    • [x] comment on line 100 (slice_equirect)

    datatypes

    • [x] more comments on some of the methods in PointDict
    opened by reubenlindroos 0
  • Documentation pipeline update

    Documentation pipeline update

    Pipeline to automatically create documenation on read the docs using doxygen.

    • [x] link to github page on index.html (mainpage.hpp)
    • [x] add documentation convention to docs\README.md
    • [x] fork branch from cr333/main and apply changes to that
    • [x] create new PR from forked branch
    • [x] remove documentation for header comment block in docs/README.md
    • [x] cleanup index.rst (try removing, see if sphinx can build anyway)
    • [ ] mainpage.hpp cleanup (capitalise, centralise)
    • [x] clarify line 48 in README.md
    opened by reubenlindroos 0
  • Demo updated

    Demo updated

    Converts demo documentation files from rst and based in sphinx to be hosted in Github. The sites markdown API now renders the documentation files rather than using sphinx + rtd.

    opened by reubenlindroos 0
  • Adds build test to master branch on push and PR

    Adds build test to master branch on push and PR

    build

    • [ ] change actions to not send email for every build
    • [x] fix requested changes
    • [x] make into squash merge to not mess with main branch history
    • [x] group build steps (building dependencies which ahve been left seperate for debugging purposes)
    • [x] check glog build variables in cmake (e.g BUILD_TEST should not be enabled)
    • [x] check eigen warnings in build log
    • [x] check if precompiled headers might speed up build
    • [x] check if multithread build could be used
    • [x] remove verbose flag from extraction of opencv

    test

    • [x] add test data download
    • [x] reduce size of test dataset
    • [x] check what happens on failure
    • [ ] check if we can "publish" test results (xml?)
    opened by reubenlindroos 0
  • Get problems while preprocessing

    Get problems while preprocessing

    I did download all of those binary files from here:https://github.com/cr333/OmniPhotos/releases/download/v1.1/OmniPhotos-v1.1-win10-x64.zip

    And I did put ffmpeg.exe into system Path. However, Im getting errors saying this below:

    $ ./preproc/preproc.exe -c preproc-config-template.yaml [23276] Failed to execute script 'main' due to unhandled exception! Traceback (most recent call last): File "main.py", line 24, in File "preproc_app.py", line 39, in init File "data_preprocessor.py", line 32, in init File "abs_preprocessor.py", line 70, in init File "abs_preprocessor.py", line 225, in load_origin_data_info File "ffmpeg_probe.py", line 20, in probe File "subprocess.py", line 800, in init File "subprocess.py", line 1207, in _execute_child FileNotFoundError: [WinError 2]

    image

    opened by BlairLeng 2
Releases(v1.1)
Owner
Christian Richardt
Christian Richardt
PyTorch IPFS Dataset

PyTorch IPFS Dataset IPFSDataset(Dataset) See the jupyter notepad to see how it works and how it interacts with a standard pytorch DataLoader You need

Jake Kalstad 2 Apr 13, 2022
Scalable Graph Neural Networks for Heterogeneous Graphs

Neighbor Averaging over Relation Subgraphs (NARS) NARS is an algorithm for node classification on heterogeneous graphs, based on scalable neighbor ave

Facebook Research 67 Dec 03, 2022
GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion

GarmentNets This repository contains the source code for the paper GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape

Columbia Artificial Intelligence and Robotics Lab 43 Nov 21, 2022
Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Sifei Liu 167 Dec 14, 2022
Replication Code for "Self-Supervised Bug Detection and Repair" NeurIPS 2021

Self-Supervised Bug Detection and Repair This is the reference code to replicate the research in Self-Supervised Bug Detection and Repair in NeurIPS 2

Microsoft 85 Dec 24, 2022
Text mining project; Using distilBERT to predict authors in the classification task authorship attribution.

DistilBERT-Text-mining-authorship-attribution Dataset used: https://www.kaggle.com/azimulh/tweets-data-for-authorship-attribution-modelling/version/2

1 Jan 13, 2022
The first public PyTorch implementation of Attentive Recurrent Comparators

arc-pytorch PyTorch implementation of Attentive Recurrent Comparators by Shyam et al. A blog explaining Attentive Recurrent Comparators Visualizing At

Sanyam Agarwal 150 Oct 14, 2022
Answering Open-Domain Questions of Varying Reasoning Steps from Text

This repository contains the authors' implementation of the Iterative Retriever, Reader, and Reranker (IRRR) model in the EMNLP 2021 paper "Answering Open-Domain Questions of Varying Reasoning Steps

26 Dec 22, 2022
Решения, подсказки, тесты и утилиты для тренировки по алгоритмам от Яндекса.

Решения и подсказки к тренировке по алгоритмам от Яндекса Что есть внутри Решения с подсказками и комментариями; рекомендую сначала смотреть md файл п

Yankovsky Andrey 50 Dec 26, 2022
An implementation of the paper "A Neural Algorithm of Artistic Style"

A Neural Algorithm of Artistic Style implementation - Neural Style Transfer This is an implementation of the research paper "A Neural Algorithm of Art

Srijarko Roy 27 Sep 20, 2022
Deep learning (neural network) based remote photoplethysmography: how to extract pulse signal from video using deep learning tools

Deep-rPPG: Camera-based pulse estimation using deep learning tools Deep learning (neural network) based remote photoplethysmography: how to extract pu

Terbe Dániel 138 Dec 17, 2022
LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs.

LocUNet LocUNet is a deep learning method to localize a UE based solely on the reported signal strengths from a set of BSs. The method utilizes accura

4 Oct 05, 2022
ML-Decoder: Scalable and Versatile Classification Head

ML-Decoder: Scalable and Versatile Classification Head Paper Official PyTorch Implementation Tal Ridnik, Gilad Sharir, Avi Ben-Cohen, Emanuel Ben-Baru

189 Jan 04, 2023
TensorFlow-based implementation of "ICNet for Real-Time Semantic Segmentation on High-Resolution Images".

ICNet_tensorflow This repo provides a TensorFlow-based implementation of paper "ICNet for Real-Time Semantic Segmentation on High-Resolution Images,"

HsuanKung Yang 406 Nov 27, 2022
for a paper about leveraging discourse markers for training new models

TSLM-DISCOURSE-MARKERS Scope This repository contains: (1) Code to extract discourse markers from wikipedia (TSA). (1) Code to extract significant dis

International Business Machines 6 Nov 02, 2022
Face Mask Detection on Image and Video using tensorflow and keras

Face-Mask-Detection Face Mask Detection on Image and Video using tensorflow and keras Train Neural Network on face-mask dataset using tensorflow and k

Nahid Ebrahimian 12 Nov 11, 2022
Create animations for the optimization trajectory of neural nets

Animating the Optimization Trajectory of Neural Nets loss-landscape-anim lets you create animated optimization path in a 2D slice of the loss landscap

Logan Yang 81 Dec 25, 2022
Computing Shapley values using VAEAC

Shapley values and the VAEAC method In this GitHub repository, we present the implementation of the VAEAC approach from our paper "Using Shapley Value

3 Nov 23, 2022
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

David Álvarez de la Torre 0 Feb 09, 2022
Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

Sicheng 19 Dec 07, 2022