added to lit review, fps/latency results

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@online{arcore-unity, @online{arcore-unity,
author = {Google}, author = {Google},
date = {2018-02-23}, date = {2018-02-23},
organization = {Google},
title = {ARCore Unity SDK}, title = {ARCore Unity SDK},
url = {https://github.com/google-ar/arcore-unity-sdk}, url = {https://github.com/google-ar/arcore-unity-sdk},
urldate = {2020-03-27} urldate = {2020-03-27}
@ -452,3 +453,92 @@
year = {2005} year = {2005}
} }
@article{visual-hull-laurentini,
author = {Laurentini, Aldo},
doi = {10.1109/34.273735},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
journaltitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
month = feb,
number = {2},
pages = {150--162},
title = {The visual hull concept for silhouette-based image understanding},
url = {https://ieeexplore.ieee.org/abstract/document/273735},
urldate = {2020-05-06},
volume = {16},
year = {1994}
}
@inproceedings{cuda-visual-hull,
abstract = {In this paper we present two efficient GPU-based visual hull computation algorithms. We compare them in terms of performance using image sets of varying size and different voxel resolutions. In addition, we present a real-time 3D reconstruction system which uses the proposed GPU-based reconstruction method to achieve real-time performance (30 fps) using 16 cameras and 4 PCs.},
author = {{Ladikos}, A. and {Benhimane}, S. and {Navab}, N.},
booktitle = {2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
doi = {10.1109/CVPRW.2008.4563098},
isbn = {978-1-4244-2339-2},
issn = {2160-7508},
keywords = {computational geometry; image reconstruction; image resolution; visual hull computation; real-time 3D reconstruction; CUDA; GPU; image sets; voxel resolutions; real-time performance; Image reconstruction; Cameras; Real time systems; Reconstruction algorithms; Personal communication networks; Shape; Hardware; Image segmentation; Distributed computing; Computer science},
month = {June},
pages = {1--8},
publisher = {IEEE},
title = {Efficient visual hull computation for real-time 3D reconstruction using CUDA},
url = {https://ieeexplore.ieee.org/abstract/document/4563098},
urldate = {2020-05-06},
year = {2008}
}
@article{microsoft-mixed-reality-studios,
address = {New York, NY, USA},
articleno = {69},
author = {Collet, Alvaro and Chuang, Ming and Sweeney, Pat and Gillett, Don and Evseev, Dennis and Calabrese, David and Hoppe, Hugues and Kirk, Adam and Sullivan, Steve},
doi = {10.1145/2766945},
issn = {0730-0301},
issue_date = {August 2015},
journal = {ACM Trans. Graph.},
keywords = {MPEG; multi-view stereo; mesh tracking; 3D video; geometry compression; surface reconstruction},
month = jul,
number = {4},
numpages = {13},
publisher = {Association for Computing Machinery},
title = {High-Quality Streamable Free-Viewpoint Video},
url = {https://opdhsblobprod03.blob.core.windows.net/contents/503db294612a42b3b95420aaabac44cc/4090ebdf84f413793b8cc1a1271a10c5?sv=2018-03-28&sr=b&si=ReadPolicy&sig=WE0q8BmNHfMXk7lk1SvB8KuKBavTZn%2BXHdYzcNRFtDk%3D&st=2020-05-06T12%3A29%3A54Z&se=2020-05-07T12%3A39%3A54Z},
urldate = {2020-05-06},
volume = {34},
year = {2015}
}
@inproceedings{hull-correspondences,
author = {{Ahmed}, N. and {Theobalt}, C. and {Rossl}, C. and {Thrun}, S. and {Seidel}, H.},
booktitle = {2008 IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2008.4587758},
isbn = {978-1-4244-2242-5},
issn = {1063-6919},
month = jun,
pages = {1--8},
title = {Dense correspondence finding for parametrization-free animation reconstruction from video},
url = {https://ieeexplore.ieee.org/abstract/document/4587758},
urldate = {2020-05-06},
year = {2008}
}
@article{surrey-surface-capture,
author = {{Starck}, J. and {Hilton}, A.},
doi = {10.1109/MCG.2007.68},
journal = {IEEE Computer Graphics and Applications},
number = {3},
pages = {21--31},
title = {Surface Capture for Performance-Based Animation},
url = {https://ieeexplore.ieee.org/abstract/document/4178157},
urldate = {2020-05-06},
volume = {27},
year = {2007}
}
@online{structure-sensor,
author = {Occipital},
month = sep,
organization = {Occipital},
title = {Structure Sensor},
url = {https://structure.io/structure-sensor},
urldate = {2020-05-06},
year = {2013}
}