Academic Journal
IMLS-Splatting: Efficient Mesh Reconstruction from Multi-view Images via Point Representation.
| Τίτλος: | IMLS-Splatting: Efficient Mesh Reconstruction from Multi-view Images via Point Representation. |
|---|---|
| Συγγραφείς: | YANG, KAIZHI, DAI, LIU, LIU, ISABELLA, ZHANG, XIAOSHUAI, SUN, XIAOYAN, CHEN, XUEJIN, XU, ZEXIANG, SU, HAO |
| Πηγή: | ACM Transactions on Graphics; Aug2025, Vol. 44 Issue 4, p1-11, 11p |
| Θεματικοί όροι: | Point cloud, Computer vision, Least squares, Surface properties, Three-dimensional modeling |
| Περίληψη: | Multi-view mesh reconstruction has long been a challenging problem in graphics and computer vision. In contrast to recent volumetric rendering methods that generate meshes through post-processing, we propose an end-to-end mesh optimization approach called IMLS-Splatting. Our method leverages the sparsity and flexibility of point clouds to efficiently represent the underlying surface. To achieve this, we introduce a splatting-based differentiable Implicit Moving-Least Squares (IMLS) algorithm that enables the fast conversion of point clouds into SDFs and texture fields, optimizing both mesh reconstruction and rasterization. Additionally, the IMLS representation ensures that the reconstructed SDF and mesh maintain continuity and smoothness without the need for extra regularization. With this efficient pipeline, our method enables the reconstruction of highly detailed meshes in approximately 11 minutes, supporting high-quality rendering and achieving state-of-the-art reconstruction performance. Our code is available at https://github.com/SilenKZYoung/IMLS-Splatting. [ABSTRACT FROM AUTHOR] |
| Copyright of ACM Transactions on Graphics is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: IMLS-Splatting: Efficient Mesh Reconstruction from Multi-view Images via Point Representation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22YANG%2C+KAIZHI%22">YANG, KAIZHI</searchLink><br /><searchLink fieldCode="AR" term="%22DAI%2C+LIU%22">DAI, LIU</searchLink><br /><searchLink fieldCode="AR" term="%22LIU%2C+ISABELLA%22">LIU, ISABELLA</searchLink><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+XIAOSHUAI%22">ZHANG, XIAOSHUAI</searchLink><br /><searchLink fieldCode="AR" term="%22SUN%2C+XIAOYAN%22">SUN, XIAOYAN</searchLink><br /><searchLink fieldCode="AR" term="%22CHEN%2C+XUEJIN%22">CHEN, XUEJIN</searchLink><br /><searchLink fieldCode="AR" term="%22XU%2C+ZEXIANG%22">XU, ZEXIANG</searchLink><br /><searchLink fieldCode="AR" term="%22SU%2C+HAO%22">SU, HAO</searchLink> – Name: TitleSource Label: Source Group: Src Data: ACM Transactions on Graphics; Aug2025, Vol. 44 Issue 4, p1-11, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+properties%22">Surface properties</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+modeling%22">Three-dimensional modeling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multi-view mesh reconstruction has long been a challenging problem in graphics and computer vision. In contrast to recent volumetric rendering methods that generate meshes through post-processing, we propose an end-to-end mesh optimization approach called IMLS-Splatting. Our method leverages the sparsity and flexibility of point clouds to efficiently represent the underlying surface. To achieve this, we introduce a splatting-based differentiable Implicit Moving-Least Squares (IMLS) algorithm that enables the fast conversion of point clouds into SDFs and texture fields, optimizing both mesh reconstruction and rasterization. Additionally, the IMLS representation ensures that the reconstructed SDF and mesh maintain continuity and smoothness without the need for extra regularization. With this efficient pipeline, our method enables the reconstruction of highly detailed meshes in approximately 11 minutes, supporting high-quality rendering and achieving state-of-the-art reconstruction performance. Our code is available at https://github.com/SilenKZYoung/IMLS-Splatting. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of ACM Transactions on Graphics is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1145/3731210 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Point cloud Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Least squares Type: general – SubjectFull: Surface properties Type: general – SubjectFull: Three-dimensional modeling Type: general Titles: – TitleFull: IMLS-Splatting: Efficient Mesh Reconstruction from Multi-view Images via Point Representation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: YANG, KAIZHI – PersonEntity: Name: NameFull: DAI, LIU – PersonEntity: Name: NameFull: LIU, ISABELLA – PersonEntity: Name: NameFull: ZHANG, XIAOSHUAI – PersonEntity: Name: NameFull: SUN, XIAOYAN – PersonEntity: Name: NameFull: CHEN, XUEJIN – PersonEntity: Name: NameFull: XU, ZEXIANG – PersonEntity: Name: NameFull: SU, HAO IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 07300301 Numbering: – Type: volume Value: 44 – Type: issue Value: 4 Titles: – TitleFull: ACM Transactions on Graphics Type: main |
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