Three-dimensional (3D) scenes of a maize canopy can be utilized to depict its vertical structure and serve as the foundation for a 3D radiative transfer model. Since canopy structures are typically heterogeneous, evaluating their spectral response is crucial for remote-sensing inversion algorithms. Yet existing methods to model 3D canopy scenes of crops lack the high efficiency required for large-scale breeding applications. Hence, this study aims to develop a method to rapidly construct 3D canopy scenes of maize based on key structural parameters—namely, leaf area, base angle, and inclination angle—across multiple cultivars and growth stages. The accuracy of this canopy scenes modeling method is validated by comparison to a fine-scale 3D model and a multi-growth stage reflectance dataset. The root mean square error (RMSE) and normalized root mean square error (NRMSE) between the fine-scale 3D model and the constructed 3D model are less than 0.021 and 6.4%, respectively. Moreover, the RMSE and NRMSE between the simulated reflectance and measured reflectance are less than 0.042 and 11.1%, respectively. Next, we couple the 3D radiative transfer model with 3D scenes to analyze the spectral response of maize canopy structure. These results indicate that the near-infrared band was affected more by leaf area than leaf base angle, while the opposite is observed for the red-edge band. Furthermore, the middle layer contributes more to the canopy reflectance than either the upper or lower layers. Notably, two maize scenes where two structural parameters simultaneously change can elicit the same canopy spectra. The proposed method here thus offers the dual advantages of rapid modeling and high efficiency, making it highly applicable for maize and other similar crops.