## 1 Introduction  
Multi-View Stereo (MVS) is one of the fundamental tasks of 3D computer vision, which is centred on the use of camera parameters and viewpoint poses to compute the mapping relationship of each pixel in an image for dense 3D scene reconstruction. In recent years, this technology has found widespread applications in areas such as robot navigation, cultural heritage preservation through digitization, and autonomous driving. Traditional methods heavily rely on manually designed similarity metrics for reconstruction [1; 2; 3; 4]. While these approaches perform well in Lambertian surface scenarios, their effectiveness diminishes in challenging conditions characterized by complex lighting variations, lack of distinct textures, and non-Lambertian surfaces. Furthermore, these methods suffer from computational inefficiency, significantly increasing the time required for reconstructing large-scale scenes, thereby limiting practical applications. Deep learning-based MVS methods, such as [PERSON] et al. [5] employed 2D Convolutional Neural Networks (CNN) to extract image features. They utilized differentiable homography warping, 3D CNN regularization, and depth regression operations to achieve end-to-end depth map prediction. Finally, the reconstructed dense scene was obtained through depth map fusion. The introduction of CNN networks allows for better extraction of global features, with excellent performance even in scenarios with weak textures and reflective environments. Additionally, [PERSON] et al. [6], in CasMVSNet, adopt a cascading approach to construct the cost volume, gradually refining the depth value sampling range from coarse to fine. This stepwise refinement at higher feature resolutions generates more detailed depth maps, ensuring overall efficiency in reconstruction and a rational allocation of computational resources. However, conventional multi-stage MVS frameworks often lack flexibility in depth sampling, relying mostly on static or pre-defined ranges for depth value sampling. In cases where there is a deviation in depth sampling for a certain pixel, the model cannot adaptively adjust the sampling range for the next stage, leading to erroneous depth inferences. The core step of multi-view stereo vision is to construct a 3D cost volume, which can be summarized as computing the similarity between multi-view images. Existing methods mostly utilize variance [6; 7] to build the 3D cost volume. For example, [PERSON] et al. [5] have the same weights for different perspectives in matching cost volume construction and use the mean square deviation method to aggregate feature volumes from different perspectives. However, this approach overlooks pixel visibility under different viewpoints, limiting its effectiveness in dense pixel-wise matching. To address this issue, [PERSON] et al. [8] introduced context-aware convolution in the AA-RMVSNet's intra-view aggregation module to aggregate feature volumes from different viewpoints. Additionally, [PERSON] et al. [9] proposed an adaptive view aggregation module, utilizing deformable convolution networks to achieve pixel-wise and voxel-wise view aggregation with minimal memory consumption. [PERSON] et al. [10] employed a learning-based block matching aggregation module, transforming individual volume pixels into pixel blocks of a certain size and facilitating information exchange at different depths. However, directly applying regularization to the cost volume fails to facilitate communication with depth feature information from adjacent depths. With the continuous development of attention mechanisms, [PERSON] et al. [11] incorporated attention mechanisms into the feature extraction stage of the MVS network, resulting in noticeable improvements in experimental results. [PERSON] et al. [12] transformed the depth estimation problem into a correspondence problem between sequences and optimized it through self-attention and cross-attention.