S3Net: Innovating Stereo Matching and Semantic Segmentation with a
  Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery
Qingyuan Yang Guanzhou Chen Xiaoliang Tan Tong Wang Jiaqi Wang Xiaodong Zhang
Stereo matching and semantic segmentation are significant tasks in binocular
satellite 3D reconstruction. However, previous studies primarily view these as
independent parallel tasks, lacking an integrated multitask learning framework.
This work introduces a solution, the Single-branch Semantic Stereo Network
(S3Net), which innovatively combines semantic segmentation and stereo matching
using Self-Fuse and Mutual-Fuse modules. Unlike preceding methods that utilize
semantic or disparity information independently, our method dentifies and
leverages the intrinsic link between these two tasks, leading to a more
accurate understanding of semantic information and disparity estimation.
Comparative testing on the US3D dataset proves the effectiveness of our S3Net.
Our model improves the mIoU in semantic segmentation from 61.38 to 67.39, and
reduces the D1-Error and average endpoint error (EPE) in disparity estimation
from 10.051 to 9.579 and 1.439 to 1.403 respectively, surpassing existing
competitive methods. Our codes are available at:https://github.com/CVEO/S3Net.