发表机构
University of Nottingham; Taraz Metrology Ltd.（诺丁汉大学; 塔拉兹计量有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RACE-FPP，一种将深度学习角点检测集成到条纹投影轮廓术全流程的表征增强方法，通过分析误差传播并采样相位值表征投影仪，显著降低重投影误差，提升测量鲁棒性。
AI 中文摘要
条纹投影轮廓术（Fringe Projection Profilometry, FPP）需要精确的系统表征以实现可靠的三维（3D）重建；然而，表征精度在很大程度上依赖于鲁棒的棋盘格特征定位，而在诸如镜头模糊和表征目标方向等具有挑战性的成像条件下，这种定位可能会退化。现有的基于深度学习的角点检测器通常仅使用检测指标和相机重投影误差进行评估，而未考虑其对投影仪表征、相机-投影仪立体表征一致性或整体测量精度的更广泛影响。在本工作中，我们引入了一个完整的FPP表征流程，将基于深度学习的角点检测纳入标准的相机表征工作流程中。我们还通过在表征目标中白色方块的中心采样相位值来表征投影仪。所提出的框架并非将角点检测视为孤立任务，而是显式分析定位误差如何在整条FPP表征链中传播。性能评估使用检测指标（如精确率和召回率）、相机和投影仪的重投影误差以及相机和投影仪的立体表征。在一个包含干净和退化图像的混合数据集上，相机重投影误差从1.237像素降低到0.259像素，而投影仪重投影误差降低了约50%。对重建工件的尺寸评估显示，与传统流程相比，几何精度有所提高。总体而言，研究结果表明，在具有挑战性的成像条件下，系统级表征的鲁棒性增强，从而使得工业FPP测量更加可靠。
英文摘要
Fringe Projection Profilometry (FPP) requires precise system characterisation to achieve reliable three-dimensional (3D) reconstructions; however, characterisation accuracy strongly depends on robust checkerboard feature localisation, which can deteriorate under challenging imaging conditions such as lens blur and characterisation target orientations. Existing deep learning-based corner detectors are typically assessed using detection metrics and camera reprojection error alone, without considering their wider impact on projector characterisation, camera-projector stereo characterisation consistency, or overall measurement accuracy. In this work, we introduce a complete FPP characterisation pipeline that incorporates deep learning-based corner detection into the standard camera characterisation workflow. We also characterise the projector by sampling phase values at the centres of the white squares in the characterisation target. Rather than treating corner detection as an isolated task, the proposed framework explicitly analyses how localisation errors propagate throughout the entire FPP characterisation chain. Performance is evaluated using detection metrics (e.g., precision and recall), camera and projector reprojection errors, and the camera and projector stereo characterisation. Across a mixed dataset of clean and degraded images, the camera reprojection error is reduced from 1.237 pixels to 0.259 pixels, while the projector reprojection error is reduced by roughly 50%. Dimensional evaluation of reconstructed artefacts shows improved geometric accuracy compared with those resulting from the conventional pipeline. Overall, the findings indicate increased robustness of system-level characterisation under challenging imaging conditions, thereby enabling more reliable industrial FPP measurements.
Comments19 pages, 9 figures, 7 tables
