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Implicit Learning of Scene Geometry from Poses for Global Localization
In this paper, we propose to utilize these minimal available labels (. i. e, poses) to learn the underlying 3D geometry of the scene and use the geometry to estimate the 6 DoF camera pose.
Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras
In this work, we address the problem of estimating the 6 DoF camera pose relative to a global frame from a single image.
Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences
Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap.
PixSelect: Less but Reliable Pixels for Accurate and Efficient Localization
Our work addresses this particular issue and shows by exploiting an interesting concept of sparse 3D models that we can exploit discriminatory environment parts and avoid useless image regions for the sake of a single image localization.
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