🤖 AI Summary
This work addresses the physical inconsistency in conventional multi-view satellite image evaluation, which relies on unconstrained 2D matching and ignores the epipolar geometry implicitly encoded in Rational Polynomial Coefficients (RPCs). The paper proposes the first geometry-aware evaluation protocol tailored to the RPC framework: it constructs a geometrically constrained search manifold via 3D projection and employs dense matching as a proxy task to assess the local uniqueness of features within a physically plausible space. By integrating geometric constraints into foundational model evaluation for the first time, this approach reveals a decoupling between semantic consistency and geometric localization capability, and establishes a reproducible, geometry-faithful benchmark for satellite imagery. Experiments demonstrate that, under RPC-consistent evaluation, generic 2D backbone networks outperform specialized 3D-aware models, underscoring the fundamental importance of geometric constraints in task formulation.
📝 Abstract
Standardized evaluation protocols are indispensable for robust benchmarking in remote sensing, particularly as foundation features are increasingly transferred across diverse sensors and complex imaging geometries. In satellite multi-view reconstruction, conventional evaluations relying on unconstrained 2D global matching are often misleading. The Rational Function Model (RFM) and its Rational Polynomial Coefficients (RPC) dictate a curved, height-dependent epipolar geometry that render flat 2D search spaces physically inconsistent. We propose a geometry-faithful and reproducible protocol tailored for the RPC framework. Our approach integrates an RPC-projected 3D consistency metric with a geometry-constrained dense matching proxy, specifically evaluating whether similarity responses remain localized and unique under physically plausible search manifolds. A pivotal finding of our joint reporting strategy is the decoupling of semantic agreement and geometric localization: high cross-view similarity at a projected 3D point does not guarantee reliable matchability in practical inference. Our benchmark demonstrates that incorporating geometric constraints is fundamental to the problem definition in satellite imagery. Furthermore, we show that state-of-the-art 2D backbones remain remarkably competitive against specialized 3D-aware models when subjected to this RPC-consistent evaluation.