Learning-Based Multiview Stereo for Remote Sensed Imagery With Relative Depth

The depth map estimation from a set of remote sensed images has been a challenging task due to the complexity of the real-world scenes, yet it is of great importance for applications such as 3-D reconstruction and digital surface model (DSM). Most of the existing learning-based multiview stereo (MVS) approaches reconstruct depth map through supervised models that are trained with large amounts of data without any prior knowledge about the areas of interest. A recent foundational model, Depth Anything (DAM) provides a robust method to estimate the relative depth map (RDM) based on single optical image. Leveraging its excellent performance in depth estimation and fine generalization capability, we propose an RDM fusion module that can be integrated with most state-of-the-art (SOTA) learning-based MVS frameworks to improve their performance in 3-D reconstruction. Extensive experiments are conducted to verify the effectiveness of the proposed module and positive results indicate that the integration of the proposed module leads to better accuracy and completeness compared with the benchmark models. The codes are available at https://github.com/2022hong/RDM-MVS/tree/main.