360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries
Authors: Huajian Huang, Changkun Liu, Yipeng Zhu, Hui Cheng, Tristan Braud, Sai-Kit Yeung

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. Project Page and dataset: https://huajianup.github.io/research/360Loc/.