CLONE: New Framework for Estimating Surface Normals from Single Images Using 3D Gaussian Splatting
Researchers have proposed CLONE, a closed-loop differentiable optimization framework that estimates surface normals from a single image without requiring ground-truth normal supervision. The system combines 3D Gaussian Splatting, a differentiable illumination model, and a diffusion-inspired refinement network in a unified 'image-geometry-image' consistency loop. The approach addresses longstanding limitations in both discriminative and generative normal estimation methods by enabling stable gradient propagation through explicit 3D geometric parameterization.
CLONE (Closed-Loop differentiable Optimization framework for Normal Estimation) is a new computer vision method that reconstructs surface normals from a single image by constructing a self-supervising geometric consistency loop. The framework uses 3D Gaussian Splatting to explicitly parameterize scene geometry and derives differentiable surface normals through covariance eigen-decomposition, creating an analytical gradient pathway. A differentiable illumination model with a learnable light modulation kernel then maps those normals back to image radiance, allowing reprojection errors to directly supervise the 3D geometry without ground-truth normal labels. To address the limited local detail expressiveness inherent in Gaussian representations, the authors add a one-step deterministic diffusion-inspired refinement network, coordinated with global geometry via a cross-domain gating fusion mechanism. All components are jointly optimized under a single reprojection objective, forming a stable, end-to-end differentiable pipeline. The work was submitted to arXiv in August 2025 and revised in June 2026, and has not yet undergone formal peer review.
What's missing
As a preprint, CLONE has not undergone formal peer review. The abstract does not report quantitative benchmark comparisons against state-of-the-art discriminative or generative normal estimation baselines, making it difficult to assess the magnitude of improvement. Computational cost and inference speed relative to existing methods are also not discussed. The framework's performance under challenging conditions such as textureless surfaces, specular materials, or outdoor illumination variation remains uncharacterized.
What different sources said
- arXiv cs.AICenter
CLONE: A 3DGS-Based Closed-Loop Differentiable Optimization Framework for Single-Image Normal Estimation
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