Fringe projection profilometry-based 3-D reconstruction of objects with high reflectivity and low surface roughness remains a significant challenge. When measuring such glossy surfaces, specular reflection and indirect illumination often lead to severe distortion or loss of the projected fringe patterns. To address these issues, we propose a latent diffusion-based structured light for reflective objects (LD-SLRO). Phase-shifted fringe images captured from highly reflective surfaces are first encoded to extract latent representations that capture surface reflectance characteristics. These latent features are then used as conditional inputs to a latent diffusion model, which probabilistically suppresses reflection-induced artifacts and recover lost fringe information, yielding high-quality fringe images. The proposed components, including the specular reflection encoder, time-variant channel affine layer, and attention modules, further improve fringe restoration quality. In addition, LD-SLRO provides high flexibility in configuring the input and output fringe sets. Experimental results demonstrate that the proposed method improves both fringe quality and 3-D reconstruction accuracy over state-of-the-art methods, reducing the average root-mean-squared error from 1.8176 mm to 0.9619 mm.

翻译：基于条纹投影轮廓术的高反光度、低表面粗糙度物体的三维重建仍面临重大挑战。在测量此类光泽表面时，镜面反射与间接照明常导致投影条纹图案严重畸变或丢失。为解决该问题，本文提出一种面向反光物体的潜在扩散结构光方法（LD-SLRO）。该方法首先对高反光表面采集的相移条纹图像进行编码，提取捕捉表面反射特性的潜在表征；随后将这些潜在特征作为条件输入至潜在扩散模型，通过概率性抑制反射伪影并恢复丢失的条纹信息，生成高质量条纹图像。所提出的镜面反射编码器、时变通道仿射层及注意力模块进一步提升了条纹复原质量。此外，LD-SLRO在配置输入与输出条纹组方面具有高度灵活性。实验结果表明：相较于现有最优方法，所提方法在条纹质量与三维重建精度上均有显著提升，将均方根误差平均值从1.8176毫米降低至0.9619毫米。