E3CM: Epipolar-Constrained Cascade Correspondence Matching

• URL: http://arxiv.org/abs/2308.16555v1
• Date: Thu, 31 Aug 2023 08:46:12 GMT
• Title: E3CM: Epipolar-Constrained Cascade Correspondence Matching
• Authors: Chenbo Zhou, Shuai Su, Qijun Chen, Rui Fan
• Abstract summary: We introduce Epipolar-Constrained Cascade Correspondence (E3CM) as a novel explicit programming-based method. Unlike traditional methods, E3CM leverages pre-trained convolutional neural networks to match correspondence. We extensively evaluate the performance of E3CM through comprehensive experiments and demonstrate its superiority over existing methods.
• Score: 19.650006628979355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and robust correspondence matching is of utmost importance for various 3D computer vision tasks. However, traditional explicit programming-based methods often struggle to handle challenging scenarios, and deep learning-based methods require large well-labeled datasets for network training. In this article, we introduce Epipolar-Constrained Cascade Correspondence (E3CM), a novel approach that addresses these limitations. Unlike traditional methods, E3CM leverages pre-trained convolutional neural networks to match correspondence, without requiring annotated data for any network training or fine-tuning. Our method utilizes epipolar constraints to guide the matching process and incorporates a cascade structure for progressive refinement of matches. We extensively evaluate the performance of E3CM through comprehensive experiments and demonstrate its superiority over existing methods. To promote further research and facilitate reproducibility, we make our source code publicly available at https://mias.group/E3CM.

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