Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation
It addresses a data scarcity problem for urban planning and autonomous vehicle training, but is an incremental application of an existing method.
This paper tackles the scarcity of realistic ground truth images for domains like urban planning and autonomous vehicles by applying Pix2Pix to transform abstract map images into realistic ones, demonstrating accurate rendering of complex urban features.
Generative Adversarial Networks (GANs) have significantly advanced image processing, with Pix2Pix being a notable framework for image-to-image translation. This paper explores a novel application of Pix2Pix to transform abstract map images into realistic ground truth images, addressing the scarcity of such images crucial for domains like urban planning and autonomous vehicle training. We detail the Pix2Pix model's utilization for generating high-fidelity datasets, supported by a dataset of paired map and aerial images, and enhanced by a tailored training regimen. The results demonstrate the model's capability to accurately render complex urban features, establishing its efficacy and potential for broad real-world applications.