Reference-Induced Consensus for Selective Posed-Reference Visual Localization

• URL: http://arxiv.org/abs/2607.04722v1
• Date: Mon, 06 Jul 2026 06:48:34 GMT
• Title: Reference-Induced Consensus for Selective Posed-Reference Visual Localization
• Abstract summary: RIC-Loc is a scene-training-free posed-reference localizer.<n>It is SfM-point-map-free in its main estimator.
• Score: 11.166886974945806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present RIC-Loc (Reference-Induced Consensus localization), a scene-training-free posed-reference localizer that is SfM-point-map-free in its main estimator: it uses known reference poses, but not precomputed SfM 3D map points, query-to-map 2D-3D matches, or query-to-map PnP. A frozen VGGT pass predicts local camera poses, depth, and query-reference tracks for a query and selected references. Each reference induces one map-frame SE(3) query-pose hypothesis, robust consensus estimates the pose, and the preserved hypothesis structure yields two reliability scores: spatial dispersion and a track-conditioned covariance score. On the covariance-eligible set, the two scores are complementary for held-out, ground-truth-free failure detection across indoor, outdoor, and large-scale low-texture benchmarks: the joint policy is strongest in textured scenes and the covariance score in the low-texture regime, and the hypothesis-derived scores consistently outperform the standard retrieval-score gap and random rankings. Without per-scene training the consensus estimator remains accurate -- competitive with structure-based localization indoors and improving over a comparable feed-forward baseline -- giving an effective selective operating regime for posed-reference localization. Code is available at https://github.com/SNU-DLLAB/ric_loc.

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