U(PM)$^2$:Unsupervised polygon matching with pre-trained models for challenging stereo images

• URL: http://arxiv.org/abs/2511.05949v1
• Date: Sat, 08 Nov 2025 09:44:31 GMT
• Title: U(PM)$^2$:Unsupervised polygon matching with pre-trained models for challenging stereo images
• Abstract summary: This paper proposes a novel U(PM)$2$: low-cost unsupervised polygon matching with pre-trained models by uniting automatically learned and handcrafted features.<n>We benchmark our U(PM)$2$ on the ScanNet and SceneFlow datasets using our proposed new metric, which achieved state-of-the-art accuracy at a competitive speed and satisfactory generalization performance at low cost without any training requirement.
• Score: 2.3784282912975345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stereo image matching is a fundamental task in computer vision, photogrammetry and remote sensing, but there is an almost unexplored field, i.e., polygon matching, which faces the following challenges: disparity discontinuity, scale variation, training requirement, and generalization. To address the above-mentioned issues, this paper proposes a novel U(PM)$^2$: low-cost unsupervised polygon matching with pre-trained models by uniting automatically learned and handcrafted features, of which pipeline is as follows: firstly, the detector leverages the pre-trained segment anything model to obtain masks; then, the vectorizer converts the masks to polygons and graphic structure; secondly, the global matcher addresses challenges from global viewpoint changes and scale variation based on bidirectional-pyramid strategy with pre-trained LoFTR; finally, the local matcher further overcomes local disparity discontinuity and topology inconsistency of polygon matching by local-joint geometry and multi-feature matching strategy with Hungarian algorithm. We benchmark our U(PM)$^2$ on the ScanNet and SceneFlow datasets using our proposed new metric, which achieved state-of-the-art accuracy at a competitive speed and satisfactory generalization performance at low cost without any training requirement.

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