RIA projects covariance descriptors from the SPD manifold into Euclidean space via Riemannian mappings to preserve structural invariants for VPR, matching supervised zero-shot performance and reaching SOTA with light fine-tuning.
Vpr-bench: An open-source visual place recognition evaluation framework with quantifiable viewpoint and appearance change,
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Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition
RIA projects covariance descriptors from the SPD manifold into Euclidean space via Riemannian mappings to preserve structural invariants for VPR, matching supervised zero-shot performance and reaching SOTA with light fine-tuning.