RCCR-MVS: Reverse Context Conditioned Regularizer for High-Resolution Aerial MVS

While memory-efficient slice-wise recurrent regularization is essential for High-Resolution Aerial Multi-View Stereo (MVS), existing approaches either restrict state updates to unidirectional contexts or lack deep contextual integration across independent bidirectional passes. To use bidirectional evidence more effectively under controlled memory budgets, we propose a highly versatile Reverse-Context-Conditioned Regularizer (RCCR) that performs strongly across both single-stage and coarse-to-fine architectures. This asymmetric regularizer uses a lightweight reverse encoder to extract compressed context, which conditions a multi-scale forward recurrent encoder–decoder via a customized cell that uses an explicit evidence-state discrepancy mechanism for optimized sequential gating. We deploy this core mechanism using an efficient two-pass inference strategy that successfully avoids full volumetric storage. To formulate our complete RCCR-MVS, we augment its cascaded deployments with two complementary modules: Coverage-risk-guided Range Expansion (CRE) to prevent true depths from falling outside cascaded search intervals, and Spatial Evidence Support (SES) to mitigate noisy initial matching evidence. Extensive experiments demonstrate that RCCR-MVS achieves state-of-the-art performance among high-resolution aerial MVS methods on WHU-MVS and LuoJia-MVS while maintaining a favorable accuracy–resource trade-off. Furthermore, it demonstrates strong cross-domain and cross-resolution generalization on our newly constructed MultiResXD-MVS dataset and on the München and Vaihingen full-image scenarios.