[Submitted on 4 Nov 2026]
Title:Gaussians Can Subtract: Neural Dissolve Fields for Hybrid Scene Rendering
View a PDF of the paper titled Gaussians Can Subtract: Neural Dissolve Fields for Hybrid Scene Rendering, by Yiyan Wang and 2 other authors
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Abstract:Hybrid Mesh-Gaussian rendering combines compact textured surfaces with flexible 3D Gaussian primitives, offering an efficient alternative to purely Gaussian scene representations for structured environments. Existing scene-reconstruction hybrids commonly follow an additive formulation, where Gaussian residuals compensate for errors left by the mesh. However, not every Mesh-Gaussian conflict is caused by missing content: residual geometry errors, occlusion mismatch, texture-projection artifacts, and view-dependent appearance can make the same mesh content consistent in one view but inconsistent in another. We therefore formulate hybrid rendering as a multi-view inconsistency problem and present Gaussolve, a subtract-and-refill framework that selectively dissolves inconsistent mesh content before residual Gaussians refill the revealed regions. We introduce a lightweight Neural Dissolve Field, parameterized by scalar spherical-harmonic Gaussians, to learn where and from which viewing directions mesh content should be dissolved. A continuous transmittance fusion rule progressively exposes residual Gaussians behind the dissolved mesh, preserving their reconstruction gradients and avoiding hard visibility discontinuities. Experiments on structured and semi-structured scenes demonstrate higher visual fidelity with fewer Gaussian primitives and faster rendering. Project page: this https URL
Comments: Accepted to WACV 2027. 16 pages including supplementary material, 11 figures, 8 tables. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2611.07049 [cs.CV]
(or arXiv:2611.07049v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2611.07049
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yiyan Wang [view email] [v1] Wed, 4 Nov 2026 19:08:50 UTC (42,422 KB)
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