论文标题
强大功能的图像样式化
Image Stylization for Robust Features
论文作者
论文摘要
对于许多计算机视觉任务而言，对视点和外观变化都具有鲁棒性的本地功能至关重要。在这项工作中，我们调查了影像学图像样式是否不仅可以提高当地特征的鲁棒性，不仅是昼夜，而且还可以改善天气和季节的变化。我们表明，除了颜色增强外，图像风格是一种学习鲁棒特征的有力方法。我们评估了视觉定位基准的知识功能，尽管仅使用合成同谱法而没有地面真相3D对应关系，但尽管训练没有训练，但表现不佳。 我们使用训练有素的功能网络来竞争长期视觉本地化和基于地图的本地化，以实现竞争分数的自动驾驶挑战。
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization improves robustness of local features to not only day-night, but also weather and season variations. We show that image stylization in addition to color augmentation is a powerful method of learning robust features. We evaluate learned features on visual localization benchmarks, outperforming state of the art baseline models despite training without ground-truth 3D correspondences using synthetic homographies only. We use trained feature networks to compete in Long-Term Visual Localization and Map-based Localization for Autonomous Driving challenges achieving competitive scores.
