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New framework uses 3D Gaussian Splatting for single-image normal estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CLONE, a novel framework for estimating surface normals from single images. This approach utilizes 3D Gaussian Splatting to create a differentiable optimization loop that unifies explicit supervision with generative priors. The system incorporates a differentiable illumination model and a diffusion-inspired refinement network to enhance geometric details and ensure end-to-end differentiability. AI

IMPACT Introduces a new method for geometric modeling in computer vision, potentially improving 3D reconstruction and scene understanding.

RANK_REASON The cluster contains an academic paper detailing a new research framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework uses 3D Gaussian Splatting for single-image normal estimation

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The cluster contains an academic paper detailing a new research framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yanxing Liang, Yinghui Wang, Wei Li, Tao Yan, Jiaxing Shen ·

    CLONE: A 3DGS-Based Closed-Loop Differentiable Optimization Framework for Single-Image Normal Estimation

    arXiv:2508.05950v2 Announce Type: replace-cross Abstract: We propose CLONE, a 3DGS-based Closed-Loop differentiable Optimization framework for single-image Normal Estimation. The core idea is to construct an "image-geometry-image" consistency loop that unifies and jointly constra…

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