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.