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.

翻译：标准化评估协议对于遥感领域的稳健基准测试不可或缺，特别是当基础特征不断跨越多样化传感器和复杂成像几何场景时。在卫星多视角重建中，依赖无约束二维全局匹配的传统评估方法常产生误导。有理函数模型及其有理多项式系数决定了弯曲且高度相关的核线几何，使得平面二维搜索空间在物理上不具一致性。我们提出一种面向RPC框架的几何保真且可复现的评估协议。该方法将RPC投影三维一致性度量与几何约束稠密匹配代理相结合，专门评估相似性响应在物理可行的搜索流形上是否保持局部化与唯一性。联合报告策略的关键发现是语义一致性与几何定位的解耦：在投影三维点处的高跨视角相似性并不能保证实际推理中的可靠可匹配性。我们的基准测试表明，在RPC一致性评估下，将几何约束纳入问题定义是卫星影像领域的根本性要求。此外，我们发现当采用该RPC一致性评估时，最先进的二维骨干网络仍能与专用三维感知模型保持显著竞争力。