(2) In the 3rd stage, ResUNet++ is used for the first time for image refinement. Our contributions are twofold: (1) The first stage of our pipeline examines the use of alternate architectures ResNet, ResUnet++ in a framework similar to the current State-of-the-Art (SoA), leading to useful insights and comparable or improved results in some cases. Our work aims to tackle the task of generating street level view images from aerial view images on the benchmarking CVUSA dataset by a cascade pipeline consisting of three smaller stages: street view image generation, semantic segmentation map generation, and image refinement, trained together in a constrained manner in a Conditional GAN (CGAN) framework. As there is no overlap in the two views, a single stage generation network fails to capture the complex scene structure of objects in these two views. Qualitative and quantitative comparisons with existing methods show that our model outperforms all others on the KL Divergence metric and ranks amongst the best for other metrics.Ībstract = "Cross view image translation is a challenging case of viewpoint translation which involves generating the street view image when the aerial view image is given and vice versa.
U-net performs the best for street view image generation and semantic map generation as a result of the skip connections between encoders and decoders, while ResU-Net++ performs the best for image refinement because of the presence of the attention module in the decoders.
Cross view image translation is a challenging case of viewpoint translation which involves generating the street view image when the aerial view image is given and vice versa.
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