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Gaussian entropy fields: Driving adaptive sparsity in 3D Gaussian optimization.

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Title: Gaussian entropy fields: Driving adaptive sparsity in 3D Gaussian optimization.
Authors: Kuang, Hong1 (AUTHOR), Liu, Jianchen1 (AUTHOR) liujianchen@sdust.edu.cn
Source: ISPRS Journal of Photogrammetry & Remote Sensing. Jun2026, Vol. 236, p273-285. 13p.
Subjects: Surface reconstruction, Three-dimensional modeling, Three-dimensional imaging, Geometric modeling, Mathematical regularization, Mathematical optimization, Image quality in imaging systems
Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading technique for novel view synthesis, demonstrating exceptional rendering efficiency. Well-reconstructed surfaces can be characterized by low configurational entropy, where dominant primitives clearly define surface geometry while redundant components are suppressed. Three complementary technical contributions are introduced: (1) entropy-driven surface modeling via entropy minimization for low configurational entropy in primitive distributions; (2) adaptive spatial regularization using the Surface Neighborhood Redundancy Index (SNRI) and image entropy-guided weighting; (3) multi-scale geometric preservation through competitive cross-scale entropy alignment. Extensive experiments demonstrate that GEF achieves competitive geometric precision on DTU and T&T benchmarks, while delivering superior rendering quality compared to existing methods on Mip-NeRF 360. Notably, superior Chamfer Distance (0.64) on DTU and F1 score (0.44) on T&T are obtained, alongside the best SSIM (0.855) and LPIPS (0.136) among baselines on Mip-NeRF 360, validating the framework's ability to enhance surface reconstruction accuracy without compromising photometric fidelity. [ABSTRACT FROM AUTHOR]
Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. 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.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 193310683
AccessLevel: 6
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PubTypeId: academicJournal
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IllustrationInfo
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  Data: Gaussian entropy fields: Driving adaptive sparsity in 3D Gaussian optimization.
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  Data: <searchLink fieldCode="AR" term="%22Kuang%2C+Hong%22">Kuang, Hong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Jianchen%22">Liu, Jianchen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liujianchen@sdust.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ISPRS+Journal+of+Photogrammetry+%26+Remote+Sensing%22">ISPRS Journal of Photogrammetry & Remote Sensing</searchLink>. Jun2026, Vol. 236, p273-285. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Surface+reconstruction%22">Surface reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+modeling%22">Three-dimensional modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+imaging%22">Three-dimensional imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Geometric+modeling%22">Geometric modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+regularization%22">Mathematical regularization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+in+imaging+systems%22">Image quality in imaging systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 3D Gaussian Splatting (3DGS) has emerged as a leading technique for novel view synthesis, demonstrating exceptional rendering efficiency. Well-reconstructed surfaces can be characterized by low configurational entropy, where dominant primitives clearly define surface geometry while redundant components are suppressed. Three complementary technical contributions are introduced: (1) entropy-driven surface modeling via entropy minimization for low configurational entropy in primitive distributions; (2) adaptive spatial regularization using the Surface Neighborhood Redundancy Index (SNRI) and image entropy-guided weighting; (3) multi-scale geometric preservation through competitive cross-scale entropy alignment. Extensive experiments demonstrate that GEF achieves competitive geometric precision on DTU and T&T benchmarks, while delivering superior rendering quality compared to existing methods on Mip-NeRF 360. Notably, superior Chamfer Distance (0.64) on DTU and F1 score (0.44) on T&T are obtained, alongside the best SSIM (0.855) and LPIPS (0.136) among baselines on Mip-NeRF 360, validating the framework's ability to enhance surface reconstruction accuracy without compromising photometric fidelity. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ISPRS Journal of Photogrammetry & Remote Sensing is the property of Elsevier B.V. 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193310683
RecordInfo BibRecord:
  BibEntity:
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      – Type: doi
        Value: 10.1016/j.isprsjprs.2026.04.010
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 273
    Subjects:
      – SubjectFull: Surface reconstruction
        Type: general
      – SubjectFull: Three-dimensional modeling
        Type: general
      – SubjectFull: Three-dimensional imaging
        Type: general
      – SubjectFull: Geometric modeling
        Type: general
      – SubjectFull: Mathematical regularization
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Image quality in imaging systems
        Type: general
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      – TitleFull: Gaussian entropy fields: Driving adaptive sparsity in 3D Gaussian optimization.
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            NameFull: Kuang, Hong
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            NameFull: Liu, Jianchen
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 236
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            – TitleFull: ISPRS Journal of Photogrammetry & Remote Sensing
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