June 10, 2024, 4:48 a.m. | Jie Deng, Wenhao Chai, Junsheng Huang, Zhonghan Zhao, Qixuan Huang, Mingyan Gao, Jianshu Guo, Shengyu Hao, Wenhao Hu, Jenq-Neng Hwang, Xi Li, Gaoang W

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.04983v1 Announce Type: new
Abstract: City scene generation has gained significant attention in autonomous driving, smart city development, and traffic simulation. It helps enhance infrastructure planning and monitoring solutions. Existing methods have employed a two-stage process involving city layout generation, typically using Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), or Transformers, followed by neural rendering. These techniques often exhibit limited diversity and noticeable artifacts in the rendered city scenes. The rendered scenes lack variety, resembling the training images, resulting in …

abstract adversarial arxiv attention autoencoders autonomous autonomous driving city cs.cv development driving gans generative generative adversarial networks infrastructure monitoring networks planning process simulation smart smart city solutions stage traffic transformers type variational autoencoders

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