Driven by deep learning, object recognition has recently made a tremendous leap forward. Nonetheless, its accuracy often still suffers from several sources of variation that can be found in real-world images. Some of the most challenging variations are induced by changing lighting conditions. This paper presents a novel approach for tackling brightness variation in the domain of 2D object detection and 6D object pose estimation. Existing works aiming at improving robustness towards different lighting conditions are often grounded on classical computer vision contrast normalisation techniques or the acquisition of large amounts of annotated data in order to achieve invariance during training. While the former cannot generalise well to a wide range of illumination conditions, the latter is neither practical nor scalable. Hence, We propose the usage of Generative Adversarial Networks in order to learn how to normalise the illumination of an input image. Thereby, the generator is explicitly designed to normalise illumination in images so to enhance the object recognition performance. Extensive evaluations demonstrate that leveraging the generated data can significantly enhance the detection performance, outperforming all other state-of-the-art methods. We further constitute a natural extension focusing on white balance variations and introduce a new dataset for evaluation.
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Driven by deep learning, object recognition has recently made a tremendous leap forward. Nonetheless, its accuracy often still suffers from several sources of variation that can be found in real-world images. Some of the most challenging variations are induced by changing lighting conditions. This paper presents a novel approach for tackling brightness variation in the domain of 2D object detection and 6D object pose estimation. Existing works aiming at improving robustness towards different lig...
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