用扩散模型修复高反光物体的条纹图像，提升三维重建精度
LD-SLRO: Latent Diffusion Structured Light for 3-D Reconstruction of Highly Reflective Objects
- 用潜空间编码器提取反光表面特征，作为扩散模型的条件输入
- 将条纹失真率降低至0.9619mm，比现有方法提升近50%
- 适合需要高精度重建的工业检测与逆向工程场景
基于条纹投影的高反光、低粗糙度物体三维重建仍面临严峻挑战。测量此类镜面表面时，镜面反射和间接光照常导致投影条纹严重失真或丢失。为此，本文提出基于潜空间扩散的结构光方法（LD-SLRO）。从高反光表面捕获的相移条纹图像首先通过编码器提取蕴含表面反射特性的潜空间表示，再作为条件输入驱动潜空间扩散模型，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。所提组件包括镜面反射编码器、时变通道仿射层及注意力模块，进一步提升条纹复原质量。此外，LD-SLRO支持灵活配置输入与输出条纹集。实验表明，该方法在条纹质量和三维重建精度上均优于现有最优方法，平均均方根误差由1.8176 mm降至0.9619 mm。
原文摘要 · Abstract (English)
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.
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