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However, the reconstruction quality of NeRFs suffers significantly from out-of-focus regions in the input images. We propose NeRF-FF, a plug-in strategy that estimates image masks based on Focus Frustums (FFs), i.e., the visible volume in the scene space that is in-focus. NeRF-FF enables a subsequently trained NeRF model to omit out-of-focus image regions during the training process. Existing methods to mitigate the effects of defocus blurred input images often leverage dynamic ray generation. This makes them incompatible with the static ray assumptions employed by runtime-performance-optimized NeRF variants, such as Instant-NGP, leading to high training times. Our experiments show that NeRF-FF outperforms state-of-the-art approaches regarding training time by two orders of magnitude\u2014reducing it to under 1\u00a0min on end-consumer hardware\u2014while maintaining comparable visual quality.\n<\/jats:p>","DOI":"10.1007\/s00371-024-03507-y","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T23:04:01Z","timestamp":1719875041000},"page":"5043-5055","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["NeRF-FF: a plug-in method to mitigate defocus blur for runtime optimized neural radiance fields"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2445-9081","authenticated-orcid":false,"given":"Tristan","family":"Wirth","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6385-3455","authenticated-orcid":false,"given":"Arne","family":"Rak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0036-319X","authenticated-orcid":false,"given":"Max","family":"von Buelow","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6993-5099","authenticated-orcid":false,"given":"Volker","family":"Knauthe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6413-0061","authenticated-orcid":false,"given":"Arjan","family":"Kuijper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7756-0901","authenticated-orcid":false,"given":"Dieter W.","family":"Fellner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"3507_CR1","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. 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