Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimation

[2508.05950] Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimation Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2508.05950 (cs) [Submitted on 8 Aug 2025 ( v1 ), last revised 15 Aug 2026 (this version, v4)] Title: Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimation Authors: Yanxing Liang , Yinghui Wang , Wei Li , Tao Yan , Jiaxing Shen View a PDF of the paper titled Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimation, by Yanxing Liang and 4 other authors View PDF HTML (experimental) Abstract: We propose CLONE, a Continuous Latent Optimization framework for Normal Estimation via 3D Gaussian splatting. The core idea is to construct an image-geometry-image consistency strategy that unifies explicit geometric representation with differentiable rendering, thereby enabling weakly supervised learning without normal ground truth. Specifically, CLONE comprises four components. First, by introducing a differentiable light interaction model with a learnable modulation kernel, we perform a unified reparameterization of the 3DGS parameter space. Second, the conditional single-step deterministic refinement network integrates denoising architectures with differentiable reprojection constraints to refine the initial normals, thereby adaptively recovering the high-frequency details erased by the inherently smooth Gaussian primitives. Third, the cross-domain gating fusion mechanism adaptively combines the two complementary normal estimates, reconciling the geometrically consistent yet over-smooth 3DGS estimate with the detailed yet potentially geometry-inconsistent refinement. Finally, all components are jointly optimized under a unified photometric reprojection objective with geometric consistency regularizations in a fully differentiable pathway, achieving an end-to-end optimization closed loop without relying on external normal labels. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2508.05950 [cs.CV] (or arXiv:2508.05950v4 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.05950 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yanxing Liang [ view email ] [v1] Fri, 8 Aug 2025 02:32:33 UTC (29,271 KB) [v2] Sun, 7 Jun 2026 13:39:50 UTC (28,881 KB) [v3] Mon, 3 Aug 2026 02:54:18 UTC (14,417 KB) [v4] Sat, 15 Aug 2026 02:52:22 UTC (13,261 KB) Full-text links: Access Paper: View a PDF of the paper titled Reprojection-Guided 3D Gaussian Splatting Diffusion for Weakly Supervised Single-Image Normal Estimation, by Yanxing Liang and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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