Abstract: Customizing terrain based on user semantics has practical value in the virtual terrain modeling of military simulation applications. This study provides a terrain surface texture generation network (TSTG-Net) that can synthesize realistic terrain based on user input semantics. TSTG-Net is designed as a Pix2pix structure and is based on CGAN. It learns the topology of customized terrain by encoding and parsing user semantics and regards the semantics feature as the constraint of CGAN. In the generator-discriminator structure, user-customized semantics are used as the input, and the real terrain with semantics is employed as the ground truth in network optimization. In order to obtain more detailed terrain information, the terrain generation strategy based on wavelet transform is studied, which takes full use of the wavelet transformation analysis method to solve the problem of terrain synthesis. TSTGNet is designed as double encoding. The first encoding-decoding process takes ordinary 4-layer encoding and 4-layer decoding structures, and the second encoding process is designed based on DWT to extract more precise features of the texture surface. Different from the state of the art, our double encoding structure can generate the terrain texture map as realistically as possible, and then finer terrain textures can be generated through the constraints conditions. In addition, it extracts the redundant features of multiple channels, and a lightweight model with lower complexity can be obtained. Experiments carried out on the public real terrain data and the synthesized terrain data both verify that the proposed algorithm can achieve user terrain customization and generate realistic results in better time performance.
Gao, Yan; Li, Jimeng; Xu, Jianzhong; and Quan, Hongyan
"Terrain Surface Texture Generation Networks for User Semantics Customization,"
Journal of System Simulation: Vol. 35:
10, Article 1.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss10/1
Gao Yan, Li Jimeng, Xu Jianzhong, et al. Terrain Surface Texture Generation Networks for User Semantics Customization[J]. Journal of System Simulation, 2023, 35(10): 2077-2086
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