Zhang Xingxing, Wang Ling
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China.
Sensors (Basel). 2025 Sep 3;25(17):5466. doi: 10.3390/s25175466.
Robust and accurate line segment matching remains a critical challenge in stereo vision, particularly in space-based applications where weak texture, structural symmetry, and strong illumination variations are common. This paper presents a multi-constraint progressive matching framework that integrates epipolar geometry, coplanarity verification, local homography, angular consistency, and distance-ratio invariance to establish reliable line correspondences. A unified cost matrix is constructed by quantitatively encoding these geometric residuals, enabling comprehensive candidate evaluation. To ensure global consistency and suppress mismatches, the final assignment is optimized using a Hungarian algorithm under one-to-one matching constraints. Extensive experiments on a wide range of stereo image pairs demonstrate that the proposed method consistently outperforms several advanced conventional approaches in terms of accuracy, robustness, and computational efficiency, as evidenced by both quantitative and qualitative evaluations.
在立体视觉中，稳健且精确的线段匹配仍然是一项关键挑战，特别是在天基应用中，那里弱纹理、结构对称和强光照变化很常见。本文提出了一种多约束渐进匹配框架，该框架整合了对极几何、共面性验证、局部单应性、角度一致性和距离比不变性，以建立可靠的线段对应关系。通过对这些几何残差进行定量编码来构建统一的代价矩阵，从而实现全面的候选评估。为确保全局一致性并抑制不匹配，在一对一匹配约束下使用匈牙利算法对最终分配进行优化。对各种立体图像对进行的大量实验表明，所提出的方法在准确性、稳健性和计算效率方面始终优于几种先进的传统方法，定量和定性评估均证明了这一点。
