CLONE: New Framework for Estimating Surface Normals from Single Images Using 3D Gaussian Splatting
Researchers have developed CLONE, a new computational framework that estimates surface normals (geometric orientation information) from single images by combining 3D Gaussian Splatting with differentiable optimization. The method creates a closed-loop consistency check between image data and geometric models, addressing limitations in both discriminative and generative approaches. This advance could improve 3D reconstruction and computer vision applications that rely on accurate surface geometry estimation.
CLONE introduces a novel approach to single-image normal estimation by constructing an "image-geometry-image" consistency loop that jointly constrains both discriminative and generative methods. The framework uses 3D Gaussian Splatting to explicitly model scenes and derive continuous, differentiable surface normals through covariance eigen-decomposition, creating a direct gradient pathway for geometric optimization. A differentiable illumination model with learnable light modulation maps surface normals to image radiance, allowing reprojection errors to directly supervise the underlying 3D geometry. To address the limited local detail expressiveness of Gaussian representations, the researchers designed a one-step diffusion-inspired refinement network that enhances geometric details while maintaining end-to-end differentiability. A cross-domain gating fusion mechanism coordinates global geometric consistency with local detail reconstruction, and all components are jointly optimized under a unified reprojection objective, enabling stable gradient propagation without requiring ground-truth normal supervision.
What's missing
The paper does not provide quantitative experimental results, benchmarks, or comparisons with existing methods. Specific performance metrics, evaluation datasets, and computational requirements are not detailed in the abstract. The practical applicability and limitations of the approach on real-world images versus synthetic data are not discussed.
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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