PatchMatch-based Multi-View Stereo （MVS） method can estimate the depth of a scene based on multiple input images and is currently applied in large-scale 3D scene reconstruction. However， the existing methods have lower accuracy and completeness in depth estimation in low-texture regions due to unstable feature matching and unreliable reliance on photometric consistency alone. To address the above problems， an MVS method based on quadtree prior assistance was proposed. Firstly， the image pixel values were used to obtain local textures. Secondly， a coarse depth map was obtained by Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection （ACMH）， which combined the structural information in the low-texture region to generate a priori plane hypothesis by using quadtree segmentation. Thirdly， by integrating the above information， a new multi-view matching cost function was designed to guide the low-texture regions for obtaining the best depth assumption， thereby improving the accuracy of stereo matching. Finally， comparison experiments were conducted with many existing traditional MVS methods on ETH3D， Tanks and Temples， and Chinese Academy of Sciences' ancient architecture datasets. The results demonstrate that the proposed method performs better， especially in ETH3D test dataset with error threshold of 2 cm， its F1 score and completeness are improved by 1.29 and 2.38 percentage points， respectively， compared with the current state-of-the-art multi-scale geometric consistency guided and planar prior assisted multi-view stereo method （ACMMP）.