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In this paper, we address the problem of wide-baseline camera pose estimationfrom a group of 360$^\circ$ panoramas under upright-camera assumption. Recentwork has demonstrated the merit of deep-learning for end-to-end direct relativepose regression in 360$^\circ$ panorama pairs [11]. To exploit the benefits ofmulti-view logic in a learning-based framework, we introduce Graph-CoVis, whichnon-trivially extends CoVisPose [11] from relative two-view to globalmulti-view spherical camera pose estimation. Graph-CoVis is a novel GraphNeural Network based architecture that jointly learns the co-visible structureand global motion in an end-to-end and fully-supervised approach. Using theZInD [4] dataset, which features real homes presenting wide-baselines,occlusion, and limited visual overlap, we show that our model performscompetitively to state-of-the-art approaches.

Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation