Semantically Consistent Sim-to-Real Image Translation with Neural Networks.

ICAISC (2)(2022)

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摘要
Texture-swapping of images has industrial benefits besides artistic stylization and photo editing, e.g. simulated images could be modified to look like real ones to train Computer Vision methods. Autonomous driving research could largely benefit from this as its neural network-based perception systems need a large amount of labeled training data. However, the sim-to-real texture swapping is a demanding challenge because of the large gap between the two domains. Another requirement is that the semantic meaning of the photo should not change during the translation. We found that SOTA algorithms struggle with these expectations, so in this work, we improve a former method by taking advantage of the semantic labeling of the training datasets. We show that with our two improvements, we can better conserve the scene of the image during the sim-to-real translation while the photorealism of the output image does not significantly change.
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关键词
neural networks,translation,sim-to-real
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