# View Synthesis with Scene Recognition for Cross-View Image Localization
## 2 Related Work  
Image-Based Localization. Cross-View-Matching (CVM) techniques have been widely adopted in the application that requires image localization [18; 19; 20; 4; 5; 6], which relies on matching query images (from ground view) to a set of geo-tagged reference images. Depending on the scenarios for the image localization, query images can be 360-panorama ground view images, dashcam images with limited view angles [12], or aerial images captured by UAVs [21]. Reference images can also be aerial/satellite views or Google Street View [22; 12]. Cross-View-Matching is achieved by adopting two Siamese networks with identical network designs to extract the features from the same structure that appeared in the different views. Recent models either take advantage of polar transformation [9; 23; 24] or advanced Vision Transformer Networks [8] to achieve a better performance in matching stationary panorama images with satellite images in good light/weather conditions. SAFA [9] proposes a design of polar transformation to regenerate the reference image and produce a similar layout with prior knowledge of the two views. Refs. [5; 8] adopted vision transformers as more effective feature extractors to align objects from two views. However, such models were incapable of matching query images captured from mobile devices such as vehicle dashcams or mobile phones. Ref. [12] adopted an Autoencoder to transfer the styles between Google Street Views and dashcam images to mitigate the performance degradation caused by the various light/weather conditions. However, such one-to-one transformation has a high computation cost, a lack of control regarding the degree of transformation, and the consideration of unqualified patch images. In additional, indoor localization [25; 26; 27] also relies on the features learnt from the images. Ref. [25] proposed the use of a clustering algorithm and dynamic compensation to enhance the accuracy of indoor positioning. Ref. [26] presented a graph-based image matching approach for indoor localization, which was based on the identification of common visual features in images of indoor environments. Ref. [27] proposed a high-accuracy recognition and localization method for moving targets in an indoor environment using binocular stereo vision.  
Style Transfer (Table 1). Style transfer enables the creation of impressive and realistic synthesized images from images of different domains. DCGAN [28] is the first milestone work that proposes using Generative Adversarial Networks (GANs) to manipulate styles between images. StyleGAN and StyleGAN2 [15; 29] are developed for synthesizing high-resolution photorealistic images with fine details, such as hair, fur, and textures, as well as high-level features, such as facial features and body posture. Ref. [16] addressed issues when combining GAN and the Variational Autoencoder (VAE), which had issues discarding high-frequency details but had stable training dynamics. To fix the issues of both models, an Introspective Adversarial Network (IAN), a combination of the GAN and VAE models, was proposed, which used an interpolating mask with multiscale dilated convolution blocks and orthogonal regularization to produce small quality changes on pre-existing images. Ref. [13] proposed a variant of the autoencoder architecture that used a swapping module to swap features between two input images, allowing for precise and fine-grained control over the manipulation of image attributes such as structure and texture.