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English(EN) RACE-FPP: A Robust AI-assisted Characterisation Enhancement for Fringe Projection Profilometry

AI通过新的RACE-FPP方法提高3D扫描精度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了RACE-FPP，一种用于增强条纹投影轮廓测量法（FPP）系统表征的新型AI辅助流程。该方法将基于深度学习的角点检测集成到标准的相机表征工作流程中，解决了镜头模糊和目标方向变化等影响精度的挑战。通过分析定位误差在整个FPP表征链中的传播方式，RACE-FPP将相机重投影误差从1.237像素显著降低到0.259像素，并将投影仪重投影误差提高了约50%。这带来了更可靠的工业FPP测量和增强的3D重建几何精度。 AI

影响 增强了工业应用中3D重建的精度，可能改进制造和质量控制流程。

排序理由 该集群描述了一篇详细介绍改进特定科学测量技术新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI通过新的RACE-FPP方法提高3D扫描精度

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该集群描述了一篇详细介绍改进特定科学测量技术新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Osman Ali (Manufacturing Metrology Team, University of Nottingham, Nottingham, United Kingdom), Xiangjun Kong (Manufacturing Metrology Team, University of Nottingham, Nottingham, United Kingdom), Tibebe Yalew (Manufacturing Metrology Team, University of … ·

    RACE-FPP：一种用于边缘投影轮廓测量法的鲁棒AI辅助表征增强方法

    arXiv:2610.08213v1 Announce Type: new Abstract: Fringe Projection Profilometry (FPP) requires precise system characterisation to achieve reliable three-dimensional (3D) reconstructions; however, characterisation accuracy strongly depends on robust checkerboard feature localisatio…

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