Image Matching by Bare Homography
Fabio Bellavia

TL;DR
This paper introduces Slime, a non-deep image matching framework that models scenes with overlapping planes to improve match stability and coverage, especially in challenging scenarios.
Contribution
Slime offers a novel intermediate representation using planar homographies to enhance hybrid image matching pipelines without relying on deep learning.
Findings
Improves match stability and coverage in challenging scenes.
Performs well in both planar and non-planar scenarios.
Provides a comprehensive comparison with deep learning methods.
Abstract
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…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Vision and Imaging · Robotics and Sensor-Based Localization
MethodsPruning
