论文标题
VIDIT：用于照明传输的虚拟图像数据集
VIDIT: Virtual Image Dataset for Illumination Transfer
论文作者
论文摘要
最近，深层图像重新获得了人们的兴趣越来越大，因为它可以通过不努力的照明特定的润饰来增强照片的增强。除了审美增强和照片蒙太奇外，图像重新确定对于域适应性很有价值，无论是增加培训数据集还是标准化输入测试数据。但是，由于各种原因，准确的重新重新重新构成非常具有挑战性，例如去除和重新铸造阴影的困难以及不同表面的建模。我们提出了一个新颖的数据集，即用于照明传输（VIDIT）的虚拟图像数据集，以创建参考评估基准并推动照明操纵方法的开发。虚拟数据集不仅是迈向实现实数性能的重要一步，而且还证明了即使可以获取和可用的实际数据集也能够改善培训。 VIDIT包含300个虚拟场景用于训练，每个场景总共捕获40次：从8个同等间隔的方位角，每个场景都有5种不同的照明剂。
Deep image relighting is gaining more interest lately, as it allows photo enhancement through illumination-specific retouching without human effort. Aside from aesthetic enhancement and photo montage, image relighting is valuable for domain adaptation, whether to augment datasets for training or to normalize input test data. Accurate relighting is, however, very challenging for various reasons, such as the difficulty in removing and recasting shadows and the modeling of different surfaces. We present a novel dataset, the Virtual Image Dataset for Illumination Transfer (VIDIT), in an effort to create a reference evaluation benchmark and to push forward the development of illumination manipulation methods. Virtual datasets are not only an important step towards achieving real-image performance but have also proven capable of improving training even when real datasets are possible to acquire and available. VIDIT contains 300 virtual scenes used for training, where every scene is captured 40 times in total: from 8 equally-spaced azimuthal angles, each lit with 5 different illuminants.
