## 2 Related Work
### Learning-Based MVS  
In early research, achieving end-to-end 3D reconstruction models was addressed by [PERSON] et al. 's SurfaceNet method [21], which cleverly encoded images and camera parameters into 3D voxels, yielding significant reconstruction results. Extending this idea, [PERSON] et al. [22] proposed DeepMVS, employing plane-wise scanning sampling for each input image to construct the cost volume of the source images. To enhance the model's scalability and overcome limitations on the number of input images, a clever use of max-pooling was employed to gather and aggregate information from neighboring images, effectively addressing this challenge. [PERSON] et al. [5] introduced an end-to-end multi-view reconstruction algorithm in MVSNet, combining plane sweep stereo, differentiable homographic warping, variance matching cost volume construction, and 3D regularization. This algorithm has become the standard procedure for MVS reconstruction. Building on this foundation, [PERSON] et al. [9] proposed an adaptive view aggregation module, constructing the cost volume selectively by learning the contributions of different views. [PERSON] et al. [23] introduced a coarse-to-fine MVS method based on a cascaded structure in EPP-MVSNet, allowing more accurate aggregation of high-resolution image features. On the other hand, addressing memory consumption concerns, [PERSON] et al. [24] introduced a coarse-to-fine cost pyramid construction method, mitigating memory usage through distributed computing to enhance model efficiency. [PERSON] et al. [25] proposed R-MVSNet, utilizing a GRU structure for cost volume regularization, effectively resolving excessive memory usage issues at the expense of increased training time. [PERSON] et al. 's VA-Point-MVSNet [26] initially predicts a coarse depth map, followed by an iterative up-sampling and refinement process to generate depth maps with a narrower depth range. However, due to potential depth interval errors in the coarse estimation phase, this method performs suboptimally in high-resolution reconstruction. The coarse-to-fine strategy also struggles to capture crucial information for depth inference. In multi-stage MVS frameworks, the initial stage typically employs a fixed depth sampling range to cover the entriedepth values of the input scene. Subsequent stages then modify the depth sampling range based on the predicted depth values from the previous stage. [PERSON]'s CasMVSNet [6] gradually reduces the depth range using a reduction factor, achieving high-quality depth map inference. [PERSON] et al. 's Fast-MVSNet [27] uses a sparse cost volume to learn both sparse and high-resolution depth maps. It employs a Gaussian-Newton layer to iteratively optimize the sparse depth map and utilizes data-adaptive propagation and the Gaussian-Newton layer for high-resolution depth map optimization. [PERSON] et al. 's UCS-Net [28] uses the variance of depth space distribution to progressively narrow the depth scanning range, achieving a reasonable and fine-grained partition of depth space underlimited memory usage. [PERSON] et al. 's PatchmatchNet [29] optimizes each stage's depth sampling using an adaptive propagation and evaluation scheme. It reduces the number of depth hypotheses and removes regularization structures to improve model efficiency, though the overall performance is not highly satisfactory. With the continuous development of attention mechanisms, [PERSON] et al. [11] applied attention mechanisms to the feature extraction stage of MVS networks to capture long-term dependencies in depth inference tasks, achieving promising experimental results. [PERSON] et al. [12] formalized the depth estimation problem as a sequence-to-sequence correspondence problem. They utilized positional encoding, self-attention, and cross-view attention mechanisms to capture the features of the cost volume, enabling dense stereo estimation. [PERSON] et al. 's TransMVSNet model [30] and [PERSON] et al.