Learning Neural Antiderivatives

dc.contributor.authorRubab, Fizzaen_US
dc.contributor.authorNsampi, Ntumba Elieen_US
dc.contributor.authorBalint, Martinen_US
dc.contributor.authorMujkanovic, Felixen_US
dc.contributor.authorSeidel, Hans-Peteren_US
dc.contributor.authorRitschel, Tobiasen_US
dc.contributor.authorLeimkühler, Thomasen_US
dc.contributor.editorEgger, Bernharden_US
dc.contributor.editorGünther, Tobiasen_US
dc.date.accessioned2025-09-24T10:37:07Z
dc.date.available2025-09-24T10:37:07Z
dc.date.issued2025
dc.description.abstractNeural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators.en_US
dc.description.sectionheadersNeural and Differentiable Rendering
dc.description.seriesinformationVision, Modeling, and Visualization
dc.identifier.doi10.2312/vmv.20251230
dc.identifier.isbn978-3-03868-294-3
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.2312/vmv.20251230
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vmv20251230
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Machine learning algorithms; Image manipulation; Rendering
dc.subjectComputing methodologies → Machine learning algorithms
dc.subjectImage manipulation
dc.subjectRendering
dc.titleLearning Neural Antiderivativesen_US
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