Microscopic three-dimensional (3D) shape reconstruction is a crucial step in the field of precision manufacturing. The reconstruction process relies on the acquisition of high-resolution and dense images. Nevertheless, in the face of high efficiency requirements in complex application scenarios, inputting high-resolution dense images will result in geometrically increased computation and complexity, making it difficult to achieve efficient and low-latency real-time microscopic 3D shape reconstruction. In response to this situation, this study proposes a grouping parallelism lightweight real-time microscopic 3D shape reconstruction method GPLWS-Net. The GPLWS-Net constructs a lightweight backbone network based on a U-shaped network and accelerates the 3D shape reconstruction process with parallel group-querying. In addition, the neural network structure is re-parameterized to avoid the accuracy loss of reconstructing the microstructure. Furthermore, to supplement the lack of existing microscopic 3D reconstruction datasets, this study publicly releases a set of multi-focus microscopic 3D reconstruction dataset called Micro 3D. The label data uses multi-modal data fusion to obtain a high- precision 3D structure of the scene. The results show that the GPLWS-Net network can not only guarantee the reconstruction accuracy, but also reduce the average time of 39.15% in the three groups of public datasets and 50.55% in the Micro 3D dataset compared with the other five types of deep learning-based methods, which can achieve real-time 3D shape reconstruction of complex microscopic scenes.