Please draw a BioRender-style framework diagram for a distributed deep learning training system for academic paper publication. Requirements are as follows 16：9:Stereo vision has become a fundamental technique in robotic perception and autonomous systems, offering dense depth estimation based on geometric disparities between stereo image pairs. However, traditional point-based feature matching approaches are often sensitive to textureless regions, illumination changes, and repetitive patterns, leading to degraded performance in challenging environments.

In this project, we propose a robust line feature matching framework tailored for stereo vision systems. Instead of relying solely on point correspondences, our method extracts geometric line segments from rectified stereo images using a combination of edge detection and line fitting algorithms. These line features, being structurally meaningful and invariant to illumination shifts, provide more stable and discriminative cues for matching.

To enhance accuracy and reliability, we design a multi-stage matching pipeline that incorporates:

Descriptor-based similarity metrics for initial line candidate selection,

Epipolar and geometric consistency constraints for false match rejection,

And refinement via local disparity optimization.

The matched line segments are then utilized to guide dense disparity map generation, enabling improved depth estimation in both structured and unstructured environments. Experimental evaluations on public datasets demonstrate that our line-based stereo matching framework outperforms conventional point-based methods in terms of robustness, especially in low-texture and high-contrast scenes.

This work provides a promising pathway toward more reliable and interpretable stereo vision systems for applications such as autonomous driving, SLAM, and 3D scene reconstruction.