Refined Cascade Cost Volume for Multiview Remote Sensing Image Reconstruction

Research on remote sensing multi-view stereo has significantly advanced the development of large-scale 3D urban reconstruction. However, existing frameworks encounter challenges with blurred edge details when processing aerial image, which impedes the accuracy of depth estimation. To address these limitations, we propose RC-MVS, the deep estimation network specifically tailored for remote sensing multi-view stereo tasks. This network aims to enhance the geometric details within the view space while effectively reducing match noise, achieving high-precision depth estimation. Specifically, we introduce a refined cascade framework that integrates geometric details with semantic information, ensuring both global structural consistency and local feature expressiveness. During the feature extraction phase, we redesign the feature space construction process and introduce a denoising feature pyramid module. This module reduces feature inconsistency and employs multiple denoising strategies to purify feature representations, thereby enhancing the accuracy of the matching process. Furthermore, to achieve progressive optimization of the depth range, we propose a progressive cross-layer fusion module. This module progressively fuses low-resolution cost volumes, reducing domain shifts between different data dimensions, thereby enhancing the understanding of fine structures within the depth map and the broader context. Experimental results show that the RC-MVS model performs exceptionally well on the LuoJia-MVS and WHU datasets, achieving superior quantitative and qualitative performance.