Intersection Distance Field Collision for GPU
dc.contributor.author | Krayer, Bastian | en_US |
dc.contributor.author | Görge, Rebekka | en_US |
dc.contributor.author | Müller, Stefan | en_US |
dc.contributor.editor | Yang, Yin | en_US |
dc.contributor.editor | Parakkat, Amal D. | en_US |
dc.contributor.editor | Deng, Bailin | en_US |
dc.contributor.editor | Noh, Seung-Tak | en_US |
dc.date.accessioned | 2022-10-04T06:37:57Z | |
dc.date.available | 2022-10-04T06:37:57Z | |
dc.date.issued | 2022 | |
dc.description.abstract | We present a framework for finding collision points between objects represented by signed distance fields. Particles are used to sample the region where intersections can occur. The distance field representation is used to project the particles onto the surface of the intersection of both objects. From there information, such as collision normals and intersection depth can be extracted. This allows for handling various types of objects in a unified way. Due to the particle approach, the algorithm is well suited to the GPU. | en_US |
dc.description.sectionheaders | Fast Geometric Computation | |
dc.description.seriesinformation | Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers | |
dc.identifier.doi | 10.2312/pg.20221242 | |
dc.identifier.isbn | 978-3-03868-190-8 | |
dc.identifier.pages | 23-28 | |
dc.identifier.pages | 6 pages | |
dc.identifier.uri | https://doi.org/10.2312/pg.20221242 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/pg20221242 | |
dc.publisher | The Eurographics Association | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | CCS Concepts: Computing methodologies → Collision detection; Mesh geometry models | |
dc.subject | Computing methodologies → Collision detection | |
dc.subject | Mesh geometry models | |
dc.title | Intersection Distance Field Collision for GPU | en_US |
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