This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching. Assuming that motion flow within the scene can be approximated by local homography transformations, matches are aggregated into overlapping clusters corresponding to virtual planes using an iterative RANSAC-based approach discarding incompatible correspondences. Moreover, the underlying planar structural design provides an explicit map between local patches associated with the matches, by which optionally refine the keypoint positions through cross-correlation template matching after the patch reprojection. Finally, to enhance robustness and fault-tolerance against violations of the piece-wise planar approximation assumption, a further strategy is designed in order to minimize the relative patch distortion in the plane reprojection by introducing an intermediate homography that projects both patches into a common plane. The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches. Unlike other current comparisons, the proposed benchmark also takes into account the more general, real, and practical cases where camera intrinsics are unavailable. Experimental results demonstrate that our proposed non-deep learning, geometry-based filter is effective in presence of outliers and the optional cross-correlation refinement step is valid in the case of corner-like keypoints. Finally, this study suggests that there is still significant development potential in practical image matching solutions in the considered research direction, which could be in the future incorporated in novel deep image matching architectures.

翻译：本文提出一种模块化、非深度学习的图像匹配稀疏对应点滤波与精化方法。假设场景内的运动流可通过局部单应变换近似，本方法采用基于迭代RANSAC的流程，通过剔除不相容的对应点，将匹配点聚合至与虚拟平面对应的重叠聚类中。此外，底层平面结构设计通过匹配关联的局部图像块间显式映射关系，在图像块重投影后可选地通过互相关模板匹配精化关键点位置。最后，为增强对分段平面近似假设失效情况的鲁棒性与容错性，本文设计了一种进一步策略：通过引入将两个图像块投影至公共平面的中间单应变换，以最小化平面重投影过程中的相对图像块畸变。所提方法在标准数据集与图像匹配流程中进行了广泛评估，并与前沿方法进行了比较。与当前其他对比研究不同，本基准测试还考虑了相机内参未知这一更普遍、真实且实用的场景。实验结果表明，我们提出的非深度学习几何滤波器在存在异常值的情况下依然有效，而可选的互相关精化步骤对类角点关键点具有良好适用性。最后，本研究指出，在所述研究方向中，实用图像匹配解决方案仍具有显著发展潜力，未来可将其整合至新型深度图像匹配架构中。