This paper presents Slime, a novel non-deep image matching framework which models the scene as rough local overlapping planes. This intermediate representation sits in-between the local affine approximation of the keypoint patches and the global matching based on both spatial and similarity constraints, providing a progressive pruning of the correspondences, as planes are easier to handle with respect to general scenes. Slime decomposes the images into overlapping regions at different scales and computes loose planar homographies. Planes are mutually extended by compatible matches and the images are split into fixed tiles, with only the best homographies retained for each pair of tiles. Stable matches are identified according to the consensus of the admissible stereo configurations provided by pairwise homographies. Within tiles, the rough planes are then merged according to their overlap in terms of matches and further consistent correspondences are extracted. The whole process only involves homography constraints. As a result, both the coverage and the stability of correct matches over the scene are amplified, together with the ability to spot matches in challenging scenes, allowing traditional hybrid matching pipelines to make up lost ground against recent end-to-end deep matching methods. In addition, the paper gives a thorough comparative analysis of recent state-of-the-art in image matching represented by end-to-end deep networks and hybrid pipelines. The evaluation considers both planar and non-planar scenes, taking into account critical and challenging scenarios including abrupt temporal image changes and strong variations in relative image rotations. According to this analysis, although the impressive progress done in this field, there is still a wide room for improvements to be investigated in future research.

翻译：本文提出Slime，一种新颖的非深度学习图像匹配框架，将场景建模为粗糙的局部重叠平面。该中间表示介于关键点斑块的局部仿射近似与基于空间和相似性约束的全局匹配之间，通过平面相对于一般场景更易处理的特性，实现对对应点的渐进式修剪。Slime将图像分解为不同尺度的重叠区域，并计算松散的平面单应性矩阵。平面通过兼容匹配相互扩展，图像被分割为固定瓦片，仅保留每对瓦片中最佳的单应性矩阵。根据成对单应性矩阵提供的可容许立体构型的共识，识别稳定的匹配点。在瓦片内部，粗糙平面根据其匹配重叠程度进行合并，并进一步提取一致对应点。整个过程仅涉及单应性约束。因此，场景中正确匹配的覆盖率和稳定性均得到增强，同时提升了在挑战性场景中发现匹配点的能力，使传统混合匹配流水线能够弥补与近期端到端深度匹配方法之间的差距。此外，本文对以端到端深度网络和混合流水线为代表的近期图像匹配技术进行了全面的比较分析。评估同时涵盖平面与非平面场景，考虑了包括突发时间图像变化和相对图像旋转剧烈变化在内的关键挑战性场景。根据该分析，尽管该领域已取得显著进展，但未来研究仍有广阔的改进空间。