In this paper we present our solution to extract albedo of branded labels for e-commerce products. To this end, we generate a large-scale photo-realistic synthetic data set for albedo extraction followed by training a generative model to translate images with diverse lighting conditions to albedo. We performed an extensive evaluation to test the generalisation of our method to in-the-wild images. From the experimental results, we observe that our solution generalises well compared to the existing method both in the unseen rendered images as well as in the wild image.
翻译:在本文中,我们提出了为电子商务产品提取品牌标签的反照率解决方案。为此,我们制作了一个用于反照率提取的大型摄影现实合成数据集,随后培训了一个将具有不同照明条件的图像转换为反照率的基因模型。我们进行了广泛的评估,以测试我们方法的概括性,然后将图像转换为瞬间图像。从实验结果来看,我们发现我们的解决办法与在未见图像和野生图像中的现有方法相比都非常概括。