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Herein, we propose a framework for the generation of photorealistic synthetic datasets using High Dynamic Range Imaging (HDRI) that include all kinds of information computer vision algorithms need. Furthermore, by utilizing the proposed framework, we demonstrate cross-domain knowledge transfer in a semantic segmentation scenario. We found that deep neural networks trained with our synthetic images or with a mix of real and synthetic perform equal to or in cases better than those trained solely on real images. To our knowledge, this is the first work that uses HDRI to successfully transfer knowledge from the synthetic domain to the real world.

Cross-Domain Knowledge Transfer Using High Dynamic Range Imaging in Synthetic Datasets