# KD-PatchMatch: A Self-Supervised Training Learning-Based PatchMatch
## 2 Related Works  
**Geometry-Based Multi-View Stereo. ** Multi-view stereo (MVS) is a process of generating dense correspondences from multiple images of known camera poses to produce dense 3D point cloud reconstruction results. According to the output result representation, traditional MVS methods are classified into three categories: voxel-based reconstruction [19; 20],point-cloud-based reconstruction [21; 22], and depth-map-based reconstruction [23; 24; 25]. In contrast, the depth-map-based methods are more efficient. This class of methods is generally based on the iterative optimization of PatchMatch [23; 24; 25] to estimate the depth map. [PERSON] et al. [23] propose Gipuma, which adopts a red-black checkerboard grid propagation approach that makes full use of GPUs to achieve massively parallel operations and greatly improves the efficiency of the algorithm. [PERSON] et al. [24] propose COLMAP, which utilizes a more complex view selection strategy based on the Markov chain model. ACMM [25] proposes a multiscale geometric consistency bootstrap framework that can be used at different scales to sense remarkable information and to convey information through the geometric consistency of multiple views. Although these methods have achieved relatively good results, they still fail to match for untextured areas or non-Lambertian surfaces. **Learning-Based PatchMatch. ** To handle more complex real-world scenes, particularly without textured or reflective surfaces, recent learning-based MVS methods [2; 15; 17] use CNNs to extract robust features and perform matching, as well as post-processing by implementing the entire traditional stereo pipeline as a neural network layer, allowing the model to be trained end-to-end, greatly improving efficiency. [PERSON] et al. [2] proposed a differentiable PatchMatch module to predict the parallax range per pixel and construct a more efficient sparse cost quantity. [PERSON] et al. [15] proposed PatchMatchNet, a novel and learnable cascade formulation, which improves the PatchMatch core algorithm by introducing an adaptive approach that causes a significant drop in computation time and memory consumption. PatchMatch-RL [17] uses PatchMatch optimization to estimate pixel depth, normals, and visibility while minimizing the expected photometric cost and maximizing the likelihood of ground-truth depth and normals by using reinforcement learning. Similarly, we introduce single-view depth estimation into the PatchMatch initialization part, which enables the model to evaluate fewer depth candidates while achieving higher accuracy and combine with knowledge distillation to propose a self-supervised training pipeline that overcomes the difficulty of ground-truth depth acquisition. **Coarse-to-Fine Depth Sampling. ** Most learning-based MVS methods [12; 13; 15; 18; 19] use the same depth hypothesis candidate set for all pixels, and to obtain higher accuracy, the depth candidates should be densely sampled, but this leads to huge memory consumption and long computation time. Recent MVS approaches [12; 14; 18] use a coarse-to-fine strategy to construct multi-scale cost volumes to get around this constraint, abandoning the previous construction of cost volumes at a fixed resolution, which results in lower computation time and GPU memory consumption. Ref. [12] obtains the depth ranges of different resolution layers and their intervals according to a fixed formula. Ref. [18] obtains the depth hypotheses of different resolution layers by acquiring the points of neighboring pixel projections of the source image. Ref. [14] introduces an adaptive resolution cost-volume-based depth estimation method, where the depth hypotheses space of each layer is obtained from the uncertainty of the pixel projections of the previous layer. We introduce probabilistic depth sampling where the depth candidates for each pixel are sampled from the single-view depth probability distribution of each pixel, which allows for higher accuracy while evaluating fewer depth candidates. **Knowledge Distillation.