The Patch-based Multi-View Stereopsis (PMVS) dense reconstruction method can automatically ignore external points and obstacle points. Compared with other 3D dense reconstruction algorithms, the algorithm is more accurate, simple, and efficient. However, holes appear in areas with sparse textures, and existing candidate points selection strategies may cause edge defects and local detail distortion. Aiming to solve these problems, this study proposes a method of pixel interpolation feature point selection based on Scale Invariant Feature Transform (SIFT), which increases the feature points of texture sparse regions and makes the feature points be evenly distributed. A more reasonable candidate point selection strategy is proposed to reduce false matching. Experiments show that the proposed method can not only ensure the reconstruction effect of sparse texture regions, but also effectively eliminate mismatched points and improve reconstruction accuracy.