论文标题
使用Kapture的强大图像检索可视定位
Robust Image Retrieval-based Visual Localization using Kapture
论文作者
论文摘要
视觉定位通过使用查询图像和地图之间的对应分析来应对从图像估算相机姿势的挑战。此任务是计算和数据密集型，它在各种数据集上的方法彻底评估中构成了挑战。但是，为了在现场进一步发展，我们声称应在涵盖广泛领域品种的多个数据集上评估强大的视觉定位算法。为了促进这一点，我们介绍了Kapture，这是一种新的，灵活的，统一的数据格式和用于视觉定位和结构 - 胶片（SFM）结构的工具箱。它可以轻松使用不同的数据集以及有效且可重复使用的数据处理。为了证明这一点，我们提供了一条用于视觉定位的多功能管道，该管道有助于使用不同的本地和全局特征，3D数据（例如深度图），非视觉传感器数据（例如IMU，GPS，WIFI）和各种处理算法。使用管道的多种配置，我们在实验中显示了Kapture的多功能性。此外，我们在八个公共数据集上评估了我们的方法，在这些数据集中，它们在其中排名最高，在其中排名第一。为了培养未来的研究，我们在宽敞的BSD许可下以Kapture格式开源的本文中发布代码，模型和所有数据集。 github.com/naver/kapture，github.com/naver/kapture-localization
Visual localization tackles the challenge of estimating the camera pose from images by using correspondence analysis between query images and a map. This task is computation and data intensive which poses challenges on thorough evaluation of methods on various datasets. However, in order to further advance in the field, we claim that robust visual localization algorithms should be evaluated on multiple datasets covering a broad domain variety. To facilitate this, we introduce kapture, a new, flexible, unified data format and toolbox for visual localization and structure-from-motion (SFM). It enables easy usage of different datasets as well as efficient and reusable data processing. To demonstrate this, we present a versatile pipeline for visual localization that facilitates the use of different local and global features, 3D data (e.g. depth maps), non-vision sensor data (e.g. IMU, GPS, WiFi), and various processing algorithms. Using multiple configurations of the pipeline, we show the great versatility of kapture in our experiments. Furthermore, we evaluate our methods on eight public datasets where they rank top on all and first on many of them. To foster future research, we release code, models, and all datasets used in this paper in the kapture format open source under a permissive BSD license. github.com/naver/kapture, github.com/naver/kapture-localization
