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‎docs/index.md

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Bolun&nbsp;Wang<sup style="color:gray">\*</sup>, Zachary&nbsp;Ferguson<sup style="color:gray">\*</sup>, Teseo&nbsp;Schneider, Xin&nbsp;Jiang, Marco&nbsp;Attene, Daniele&nbsp;Panozzo <span style="color:gray;text-align:center;font-size:10pt;">(<sup>\*</sup>Joint first authors)</span>
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*ACM Transactions on Graphics 2021*
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*ACM Transactions on Graphics, 2021*
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#### Abstract
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Bolun&nbsp;Wang, Zachary&nbsp;Ferguson, Xin&nbsp;Jiang, Marco &nbsp;Attene, Daniele&nbsp;Panozzo, Teseo&nbsp;Schneider
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*Computer Graphics Forum (Eurographics) 2022*
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*Computer Graphics Forum (Eurographics), 2022*
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#### Abstract
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* [Tight Inclusion CCD](https://github.com/Continuous-Collision-Detection/Tight-Inclusion)
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* [Exact Root Parity CCD](https://github.com/Continuous-Collision-Detection/ExactRootParityCCD)
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* [Scalable CCD](https://github.com/Continuous-Collision-Detection/Scalable-CCD)
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* [Symbolic CCD](https://github.com/Continuous-Collision-Detection/Symbolic)
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* Queries:
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* [Sample](https://github.com/Continuous-Collision-Detection/Sample-Queries)
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* [Full Dataset](https://archive.nyu.edu/handle/2451/61518)
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* [Sample Broad Phase](https://github.com/Continuous-Collision-Detection/Sample-Scalable-CCD-Data)
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* [Sample Broad Phase](https://github.com/Continuous-Collision-Detection/Sample-Scalable-CCD-Data)
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## Acknowledgments
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We thank the NYU IT High Performance Computing for resources, services, and staff expertise.
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This work was partially supported by the NSF CAREER award under Grant No. 1652515; the NSF grants OAC-1835712, OIA-1937043, CHS-1908767, and CHS-1901091; National Key Research and Development Program of China No. 2020YFA0713700; Natural Science Foundation of China Grants No. 12171023 and 12001028; NSERC DGECR-2021-00461 and RGPIN-2021-03707; KAUST baseline funding (grant BAS/1/1679-01-01); EU ERC Advanced Grant CHANGE No. 694515 and EU project DIGITbrain/ProMED (952071); a Sloan Fellowship; a gift from Adobe Research; a gift from nTopology; and a gift from Advanced Micro Devices, Inc.

‎docs/root_parity.md

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<style>
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p {
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</style>
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<center>
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<h1>Fast and Exact Root Parity for Continuous Collision Detection</h1>
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Our method does not have numerical parameters to tune, has a performance comparable to efficient approximate algorithms, and is exact. We test our approach on a large collection of synthetic tests and real simulations, and we demonstrate that it can be easily integrated into existing simulators.
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## Fast Forward
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<div class="video-wrapper">
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<iframe class="youtube" src="https://www.youtube-nocookie.com/embed/3nXvf1nI23M?si=90pItnlHagyD2Qb7" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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</div>
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## Presentation
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* Watch our full Eurographics 2022 presentation on [Bilibili](https://www.bilibili.com/video/BV12U4y1U7nV/) (English)!
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<div class="video-wrapper">
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<iframe class="youtube" src="https://www.youtube-nocookie.com/embed/HoEbM0fN9Wk?si=Qf76VOh03dV-PkLj" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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</div>
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## Simulation Videos
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<figure>
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</figcaption>
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</figure>
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<!-- ## Source Code and Data -->
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## Source Code
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## Source Code and Data
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<!-- [![Build status](https://github.com/Continuous-Collision-Detection/CCD-Wrapper/workflows/Build/badge.svg)](https://github.com/Continuous-Collision-Detection/CCD-Wrapper/actions?query=workflow%3ABuild+branch%3Amaster+event%3Apush)
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[![Nightly build](https://github.com/Continuous-Collision-Detection/CCD-Wrapper/workflows/Nightly/badge.svg)](https://github.com/Continuous-Collision-Detection/CCD-Wrapper/actions?query=workflow%3ANightly+branch%3Amaster+event%3Aschedule) -->
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<a href="https://github.com/Continuous-Collision-Detection/ExactRootParityCCD/blob/master/LICENSE">
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<img src="https://img.shields.io/github/license/Continuous-Collision-Detection/ExactRootParityCCD.svg?color=blue"></img>
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</a>
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* [Code](https://github.com/Continuous-Collision-Detection/ExactRootParityCCD)
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<!-- * Queries:
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* [Sample](https://github.com/Continuous-Collision-Detection/Sample-Queries)
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* [Full Dataset](https://archive.nyu.edu/handle/2451/61518) -->
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* [Dataset of Rounded CCD Queries](https://archive.nyu.edu/handle/2451/63808)
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## BibTex
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‎docs/scalable_ccd.md

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<center>
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<h1>Time of Impact Dataset for Continuous Collision Detection and a Scalable Conservative Algorithm</h1>
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## Video
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<div class="video-wrapper">
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<iframe class="youtube" src="https://www.youtube.com/embed/ezuC9EisPII?si=3LEO_SeiMi4v7Y14" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen ></iframe>
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<iframe class="youtube" src="https://www.youtube-nocookie.com/embed/ezuC9EisPII?si=_oZJwrSBQa5zV6WT" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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</div>
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## Source Code and Data
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* [Our Scalable CCD](https://github.com/Continuous-Collision-Detection/Scalable-CCD)
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* [Algorithms and Testing Framework]() (coming soon!)
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* Queries:
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* Data:
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* [Sample](https://github.com/Continuous-Collision-Detection/Sample-Scalable-CCD-Data)
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* [Full Dataset]() (coming soon!)
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## Acknowledgments
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This work was funded by the NSF CAREER award under Grant No. 1652515, the NSF grants OAC-1835712, OIA-1937043, CHS-1908767, CHS-1901091, NSERC DGECR-2021-00461 and RGPIN 2021-03707, KAUST baseline funding (grant BAS/1/1679-01-01), by EU project DIGITbrain/ProMED (952071).
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We thank the NYU IT High Performance Computing for resources, services, and staff expertise. This work was funded by the NSF CAREER award under Grant No. 1652515, the NSF grants OAC-1835712, OIA-1937043, CHS-1908767, CHS-1901091, NSERC DGECR-2021-00461 and RGPIN 2021-03707, KAUST baseline funding (grant BAS/1/1679-01-01), by EU project DIGITbrain/ProMED (952071).

‎docs/stylesheets/extra.css

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.video-wrapper iframe.youtube {
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‎docs/tight_inclusion.md

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<h1>A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection</h1>
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By combining the seminal interval root-finding algorithm introduced by Snyder in 1992 with modern predicate design techniques, we propose a simple and efficient CCD algorithm. This algorithm is competitive with state-of-the-art methods in terms of runtime while conservatively reporting the time of impact and allowing an explicit trade-off between runtime efficiency and the number of false positives reported.
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## Fast Forward
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<div class="video-wrapper">
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<iframe class="youtube" src="https://www.youtube-nocookie.com/embed/dvbTGl5Vre8?si=YZgYkF4r9EPGVeFM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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</div>
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## Presentation
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<div class="video-wrapper">
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<iframe class="youtube" src="https://www.youtube-nocookie.com/embed/bwVIPGVvETs?si=WaY01gYqm0DcpT9m" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
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</div>
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<figure>
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* [GitHub Organization](https://github.com/Continuous-Collision-Detection)
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* [Wrapper and Benchmark](https://github.com/Continuous-Collision-Detection/CCD-Wrapper)
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* [Tight-Inclusion (Novel Inclusion-Based CCD)](https://github.com/Continuous-Collision-Detection/Tight-Inclusion)
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* [Symbolic CCD](https://github.com/Continuous-Collision-Detection/Symbolic)
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* Queries:
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* [Sample](https://github.com/Continuous-Collision-Detection/Sample-Queries)
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* [Full Dataset](https://archive.nyu.edu/handle/2451/61518)
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## Acknowledgments
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This work was partially supported by the NSF CAREER award under Grant No. 1652515, the NSF grants OAC-1835712, OIA-1937043, CHS-1908767, CHS-1901091, National Key Research and Development Program of China No. 2020YFA0713700, EU ERC Advanced Grant CHANGE No. 694515, a Sloan Fellowship, a gift from Adobe Research, a gift from nTopology, and a gift from Advanced Micro Devices, Inc.
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We thank the NYU IT High Performance Computing for resources, services, and staff expertise. This work was partially supported by the NSF CAREER award under Grant No. 1652515, the NSF grants OAC-1835712, OIA-1937043, CHS-1908767, CHS-1901091, National Key Research and Development Program of China No. 2020YFA0713700, EU ERC Advanced Grant CHANGE No. 694515, a Sloan Fellowship, a gift from Adobe Research, a gift from nTopology, and a gift from Advanced Micro Devices, Inc.

‎mkdocs.yml

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extra_css:
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- stylesheets/extra.css
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- Home: index.md
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- Tight Inclusion: tight_inclusion.md

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