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Book Chapter: SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views

TitleSparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
Authors
KeywordsReconstruction
Sparse views
Volume rendering
Issue Date11-Nov-2022
PublisherSpringer
Abstract

We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction approaches usually produce incomplete or distorted results. Moreover, their inability of generalizing to unseen new scenes impedes their application in practice. Contrarily, SparseNeuS can generalize to new scenes and work well with sparse images (as few as 2 or 3). SparseNeuS adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing geometry encoding volumes for generic surface prediction. Moreover, several strategies are introduced to effectively leverage sparse views for high-quality reconstruction, including 1) a multi-level geometry reasoning framework to recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color blending scheme for more reliable color prediction; 3) a consistency-aware fine-tuning scheme to control the inconsistent regions caused by occlusion and noise. Extensive experiments demonstrate that our approach not only outperforms the state-of-the-art methods, but also exhibits good efficiency, generalizability, and flexibility (Visit our project page: https://www.xxlong.site/SparseNeuS).


Persistent Identifierhttp://hdl.handle.net/10722/337676
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLong, Xiaoxiao-
dc.contributor.authorLin, Cheng-
dc.contributor.authorWang, Peng-
dc.contributor.authorKomura, Taku-
dc.contributor.authorWang, Wenping-
dc.date.accessioned2024-03-11T10:23:01Z-
dc.date.available2024-03-11T10:23:01Z-
dc.date.issued2022-11-11-
dc.identifier.isbn9783031198236-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/337676-
dc.description.abstract<p>We introduce <em>SparseNeuS</em>, a novel neural rendering based method for the task of surface reconstruction from multi-view images. This task becomes more difficult when only sparse images are provided as input, a scenario where existing neural reconstruction approaches usually produce incomplete or distorted results. Moreover, their inability of generalizing to unseen new scenes impedes their application in practice. Contrarily, <em>SparseNeuS</em> can generalize to new scenes and work well with sparse images (as few as 2 or 3). <em>SparseNeuS</em> adopts signed distance function (SDF) as the surface representation, and learns generalizable priors from image features by introducing <em>geometry encoding</em> volumes for generic surface prediction. Moreover, several strategies are introduced to effectively leverage sparse views for high-quality reconstruction, including 1) a multi-level geometry reasoning framework to recover the surfaces in a coarse-to-fine manner; 2) a multi-scale color blending scheme for more reliable color prediction; 3) a consistency-aware fine-tuning scheme to control the inconsistent regions caused by occlusion and noise. Extensive experiments demonstrate that our approach not only outperforms the state-of-the-art methods, but also exhibits good efficiency, generalizability, and flexibility (Visit our project page: <a href="https://www.xxlong.site/SparseNeuS">https://www.xxlong.site/SparseNeuS</a>).</p>-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofComputer Vision – ECCV 2022-
dc.subjectReconstruction-
dc.subjectSparse views-
dc.subjectVolume rendering-
dc.titleSparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views-
dc.typeBook_Chapter-
dc.identifier.doi10.1007/978-3-031-19824-3_13-
dc.identifier.scopuseid_2-s2.0-85144482296-
dc.identifier.volume13692 LNCS-
dc.identifier.spage210-
dc.identifier.epage227-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000903565400013-
dc.identifier.eisbn9783031198243-
dc.identifier.issnl0302-9743-

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