The invention discloses a simulator visual automatic modeling method based on a
convolutional neural network, and relates to the technical field of simulator
visual modeling and
computer vision. The problems that in the prior art, simulator
visual modeling is low in efficiency, poor in precision, insufficient in adaptability and low in
building modeling automation degree are solved. The method comprises the steps of visual data preprocessing, visual network initialization, structure
adaptive optimization, visual model training, visual model optimization output and optional visual model
adaptation. Wherein the scene network is initialized to construct a special
convolutional neural network framework containing a building
structure extraction sub-module, and building area segmentation and structure parameter extraction are realized; the structure self-
adaptive optimization adopts a
reinforcement learning agent model to dynamically adjust the
network structure; an improved AdamW
algorithm is adopted in the training process, and an early stop mechanism is set to avoid
overfitting; the optimization output guarantees the performance of the model through grid simplification,
texture enhancement and illumination calibration. Synchronous automatic modeling of the
terrain and the building is achieved, the modeling efficiency is improved by 80% or above, the rendering
frame rate of the final output model is not lower than 60 fps, the geometric precision error of the
terrain is smaller than or equal to 5%, the
dimensional precision error of the building is smaller than or equal to 3%, and the method can directly adapt to a
flight simulator and is especially suitable for low-altitude
flight training visual construction of a mountain-building mixed scene. The practical value and the popularization prospect are extremely high.