360Loc：一个用于跨设备查询的全向视觉定位的数据集与基准
360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries
AI总结：
本文提出360Loc数据集与基准，首次探索跨设备全向视觉定位挑战，通过虚拟相机方法缓解跨设备域差距，证明全向视觉定位在复杂场景中更鲁棒。
摘要：
便携式360度相机正成为一种廉价且高效的工具，用于建立大型视觉数据库。通过捕捉场景的全向视图，这些相机可以加速构建对视觉定位至关重要的环境模型。然而，由于缺乏有价值的数据集，这种优势经常被忽视。本文引入了一个新的基准数据集360Loc，由带有真实位姿的360度图像组成，用于视觉定位。我们提出了一种360度建图的实用实现，将360度图像与激光雷达数据相结合，以生成真实的6DoF位姿。360Loc是第一个探索跨设备视觉定位挑战的数据集和基准，涉及360度参考帧，以及来自针孔、超宽视场鱼眼和360度相机的查询帧。我们提出了一种虚拟相机方法，从360度图像生成较低视场的查询帧，这确保了在视觉定位任务中不同查询类型之间的性能公平比较。我们还将这种虚拟相机方法扩展到基于特征匹配和基于位姿回归的方法，以缓解由跨设备域差距引起的性能损失，并针对最先进的基线评估其有效性。我们证明，全向视觉定位在具有对称性和重复结构的挑战性大规模场景中更加鲁棒。这些结果为360相机建图和跨设备查询的全向视觉定位提供了新见解。
英文摘要：
Portable 360$^\circ$ cameras are becoming a cheap and efficient tool to establish large visual databases. By capturing omnidirectional views of a scene, these cameras could expedite building environment models that are essential for visual localization. However, such an advantage is often overlooked due to the lack of valuable datasets. This paper introduces a new benchmark dataset, 360Loc, composed of 360$^\circ$ images with ground truth poses for visual localization. We present a practical implementation of 360$^\circ$ mapping combining 360$^\circ$ images with lidar data to generate the ground truth 6DoF poses. 360Loc is the first dataset and benchmark that explores the challenge of cross-device visual positioning, involving 360$^\circ$ reference frames, and query frames from pinhole, ultra-wide FoV fisheye, and 360$^\circ$ cameras. We propose a virtual camera approach to generate lower-FoV query frames from 360$^\circ$ images, which ensures a fair comparison of performance among different query types in visual localization tasks. We also extend this virtual camera approach to feature matching-based and pose regression-based methods to alleviate the performance loss caused by the cross-device domain gap, and evaluate its effectiveness against state-of-the-art baselines. We demonstrate that omnidirectional visual localization is more robust in challenging large-scale scenes with symmetries and repetitive structures. These results provide new insights into 360-camera mapping and omnidirectional visual localization with cross-device queries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
