RGB-D Neural Radiance Fields: Local Sampling for Faster Training

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dc.contributor.authorDey, Arnaben_US
dc.contributor.authorComport, Andrew I.en_US
dc.contributor.editorSauvage, Basileen_US
dc.contributor.editorHasic-Telalovic, Jasminkaen_US
dc.date.accessioned2022-04-22T07:54:15Z
dc.date.available2022-04-22T07:54:15Z
dc.date.issued2022
dc.description.abstractLearning a 3D representation of a scene has been a challenging problem for decades in computer vision. Recent advancements in implicit neural representation from images using neural radiance fields(NeRF) have shown promising results. Some of the limitations of previous NeRF based methods include longer training time, and inaccurate underlying geometry. The proposed method takes advantage of RGB-D data to reduce training time by leveraging depth sensing to improve local sampling. This paper proposes a depth-guided local sampling strategy and a smaller neural network architecture to achieve faster training time without compromising quality.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEurographics 2022 - Posters
dc.identifier.doi10.2312/egp.20221001
dc.identifier.isbn978-3-03868-171-7
dc.identifier.issn1017-4656
dc.identifier.pages3-4
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/egp.20221001
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egp20221001
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 --> Appearance and texture representations
dc.subjectComputing methodologies
dc.subjectAppearance and texture representations
dc.titleRGB-D Neural Radiance Fields: Local Sampling for Faster Trainingen_US
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