相位提取与深度估计是结构光三维测量中的重点环节，目前传统方法在结构光相位提取与深度估计方面存在效率不高、结果不够鲁棒等问题。为了提高深度学习结构光的重建效果，本文提出了一种基于轻型自限制注意力（Light Self-Limited-Attention，LSLA）的结构光相位及深度估计混合网络，即构建一种CNN-Transformer的混合模块，并将构建的混合模块放入U型架构中，实现CNN与Transformer的优势互补。将所提出的网络在结构光相位估计和结构光深度估计两个任务上进行实验，并和其他网络进行对比。实验结果表明：相比其他网络，本文所提出的网络在相位估计和深度估计的细节处理上更加精细，在结构光相位估计实验中，精度最高提升31%；在结构光深度估计实验中，精度最高提升26%。该方法提高了深度神经网络在结构光相位估计及深度估计的准确性。

Phase retrieval and depth estimation are vital to three-dimensional measurement using structured light. Currently, conventional methods for structured light phase retrieval and depth estimation have limited efficiency and are lack of robustness in their results and so on. To improve the reconstruction effect of structured light by deep learning, we propose a hybrid network for structured light phase and depth estimation based on Light Self-Limited Attention (LSLA). Specifically, a CNN-Transformer hybrid module is constructed and integrated into a U-shaped structure to realize the advantages complementary of CNN and Transformer. The proposed network is experimentally compared with other networks in structured light phase estimation and structured light depth estimation. The experimental results indicate that the proposed network achieves finer detail processing in phase and depth estimation compared to other networks. Specifically, for structured light phase and depth estimation, its accuracy improves by 31% and 26%, respectively. Therefore, the proposed network improves the accuracy of deep neural networks in the structured light phase and depth estimation areas.