Structured light 3D measurement technology (SL-3D) has achieved extensive industrial applications, owing to its non-contact, high-precision, and rapid measurement capabilities. Recently, deep learning-based SL-3D has advanced quickly, outperforming traditional methods in many aspects. However, the high cost of constructing real datasets has impeded this technology’s development. To address this challenge, we have developed the first user-friendly simulation toolbox for structured light 3D measurement systems. This system generates and saves a complete set of 46 ‘.mat ’ files based on multi-frequency and N-step phase shifts within 4 s. We first introduced discontinuous geometric shapes, making simulated objects more representative of real-world scenarios. Additionally, our simulation first includes realistic measurement environment features such as shadow regions and various types of noise, including higher harmonic noise, random noise, and Gaussian noise, with controllable types and levels, closely mimicking actual measurement conditions. This toolbox holds significant potential for accelerating advancements in structured light 3D reconstruction and is publicly available at: https://github.com/LiYiMingM/Structured-Light-3D-Toolbox .