MPR-GAN: A Novel Neural Rendering Framework for MLS Point Cloud With Deep Generative Learning

Xu, Qingyang; Guan, Xuefeng; Cao, Jun; Ma, Yanli; Wu, Huayi

doi:10.1109/tgrs.2022.3212389

IEEE Trans. Geosci. Remote Sensing

2022

DOI: 10.1109/tgrs.2022.3212389

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MPR-GAN: A Novel Neural Rendering Framework for MLS Point Cloud With Deep Generative Learning

Qingyang Xu

Xuefeng Guan

Jun Cao

et al.

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Cited by 3 publications

References 52 publications

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Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

Xiao,

Luo,

et al. 2024

Int J Comput Intell Syst

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To improving ship image generation and classification tasks, a deep convolution generative adversarial network based on attention mechanism (ADCGAN) model was constructed. The rectified linear unit (ReLU) activation function was adopted, and three Deconv layers and Conv layers were added to both the generator and discriminator. Subsequently, an attention mechanism was added to the generator, while spectral normalization (SN) was added to the discriminator. Mean squared error (MSE) was used as loss function to stabilize the training process. Furthermore, ship classification tasks were performed using the generated ship images by end-to-end training of the classification network, enabling ship data augmentation and co-learning with other tasks. Experimental results on the Ship700 and Seaship7000 datasets demonstrate that the ADCGAN model can generate clear and robust ship images, with PSNR, LIPIPS, MS-SSIM values of 20.279 and 27.523, 0.596 and 0.096, 0.781 and 0.947, respectively. The effectiveness of the proposed method in ship image classification tasks was also verified, providing a data foundation for other collaborative tasks.

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Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

Xiao,

Luo,

et al. 2024

Int J Comput Intell Syst

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Simulation of Complex Geological Architectures Based on Multistage Generative Adversarial Networks Integrating With Attention Mechanism and Spectral Normalization

Liu

Chen

Cheng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Environmental Landscape Art Design Based on Visual Neural Network Model in Rural Construction

Wang¹,

He²

2023

Ecological Chemistry and Engineering S

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As the resources of social development continue to tilt to the countryside, the speed of rural construction continues to accelerate. In recent years, because of the higher quality of lifestyles, the demand of rural environment landscape art has gradually increased. In order to assist rural construction and improve the artistic quality of its environmental landscape, this paper proposes an environmental landscape art design method based on a visual neural network model. Firstly, the Swin Transformer text encoder is used to characterise the landscape art demand in rural construction. Then, the text feature vector of landscape art demand is input into the GAN model to generate the image content of rural construction. Finally, to better evaluate the landscape art level of the above methods in this paper, we propose an evaluating method for the landscape designing tasks. We conduct the experiments and achieve the FID value of 15.23, which can demonstrate that our method can effectively carry out an environmental landscape design for rural construction and simplify the process of rural construction. The landscape design evaluation method can evaluate the environmental landscape design accurately by the accuracy of over 80 %, and further improve and optimise the acceptance link of rural construction.

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MPR-GAN: A Novel Neural Rendering Framework for MLS Point Cloud With Deep Generative Learning

Cited by 3 publications

References 52 publications

Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

Visual Ship Image Synthesis and Classification Framework Based on Attention-DCGAN

Simulation of Complex Geological Architectures Based on Multistage Generative Adversarial Networks Integrating With Attention Mechanism and Spectral Normalization

Environmental Landscape Art Design Based on Visual Neural Network Model in Rural Construction

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