For deep learning-based stereo matching, despite deep convolution networks possessing rich, high-level semantic features that are usually abstract and low-resolution, it is difficult to accurately calculate per-pixel correspondence without image detail information in challenging regions, such as thin objects, edge contours, reflective surfaces, etc. Motivated by the information propagation capability of the feature pyramid network (FPN), we focus on using FPN to improve the detailed comprehension of spatial pyramid pooling (SPP). We propose a feature SPP (FSPP), which consists of FPN and SPP. FSPP integrates high-frequency details reserved by FPN into multiscale features extracted by SPP to form a feature volume considering both detailed and contextual information. Using FSPP, features become distinct and alleviate the ambiguity of matching progress in challenging regions. Furthermore, we concatenate FSPP to Pyramid Stereo Matching Network with three-dimensional convolution and hourglass architecture. We also evaluate its effectiveness on the KITTI 2012/2015 and the Middlebury 2014/2021 datasets. The results show that the proposed algorithm performs better than current state-of-the-art algorithms in challenging regions while taking on better generalization.