Scene reconstruction and visual localization in dynamic environments such as street scenes are a challenge due to the lack of distinctive, stable keypoints. While learned convolutional features have proven to be robust to changes in viewing conditions, handcrafted features still have advantages in distinctiveness and accuracy when applied to structure from motion. For collaborative reconstruction of road sections by a car fleet, we propose to use multimodal domain adaptation as a preprocessing step to align images in their appearance and enhance keypoint matching across viewing conditions while preserving the advantages of handcrafted features. Training a generative adversarial network for translations between different illumination and weather conditions, we evaluate qualitative and quantitative aspects of domain adaptation and its impact on feature correspondences. Combined with a multi-feature discriminator, the model is optimized for synthesis of images which do not only improve feature matching but also exhibit a high visual quality. Experiments with a challenging multi-domain dataset recorded in various road scenes on multiple test drives show that our approach outperforms other traditional and learning-based methods by improving completeness or accuracy of structure from motion with multimodal two-domain image collections in eight out of ten test scenes.

由于缺乏独特，稳定的关键点，因此在动态环境（例如街道场景）中的场景重建和视觉定位是一个挑战。虽然已证明学习的卷积特征对于查看条件的变化具有鲁棒性，但当将手工特征应用于运动结构时，其仍具有独特性和准确性。对于由车队进行的道路路段的协作重建，我们建议使用多峰域自适应作为预处理步骤来对齐图像的外观，并增强跨查看条件的关键点匹配，同时保留手工功能的优势。训练生成式对抗网络，以在不同光照和天气条件之间进行转换，我们评估了领域适应的定性和定量方面及其对特征对应的影响。结合多特征鉴别器，该模型针对图像合成进行了优化，该图像不仅改善了特征匹配，而且显示了很高的视觉质量。在多个试驾上在各种道路场景中记录的具有挑战性的多域数据集的实验表明，我们的方法通过使用多模式两域图像集合从运动中改善结构的完整性或准确性，从而优于其他传统方法和基于学习的方法测试场景。