Strictly Conservative Neural Implicits

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dc.contributor.authorLudwig, Ingmaren_US
dc.contributor.authorCampen, Marcelen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:09:05Z
dc.date.available2024-10-13T18:09:05Z
dc.date.issued2024
dc.description.abstractWe describe a method to convert 3D shapes into neural implicit form such that the shape is approximated in a guaranteed conservative manner. This means the input shape is strictly contained inside the neural implicit or, alternatively, vice versa. Such conservative approximations are of interest in a variety of applications, including collision detection, occlusion culling, or intersection testing. Our approach is the first to guarantee conservativeness in this context of neural implicits. We support input given as mesh, voxel set, or implicit function. Adaptive affine arithmetic is employed in the neural network fitting process, enabling the reasoning over infinite sets of points despite using a finite set of training data. Combined with an interior point style optimization approach this yields the desired guarantee.en_US
dc.description.number7
dc.description.sectionheadersCurve and Surface Modeling
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15241
dc.identifier.issn1467-8659
dc.identifier.pages11 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15241
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15241
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Shape analysis; Mesh models; Neural networks
dc.subjectComputing methodologies → Shape analysis
dc.subjectMesh models
dc.subjectNeural networks
dc.titleStrictly Conservative Neural Implicitsen_US
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