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.

翻译：准确且鲁棒的对应匹配对于多种三维计算机视觉任务至关重要。然而，传统的基于显式编程的方法往往难以应对具有挑战性的场景，而基于深度学习的方法则需要大规模高质量标注数据集进行网络训练。本文提出极线约束级联对应匹配（E3CM），一种克服上述局限性的新方法。与传统方法不同，E3CM利用预训练卷积神经网络进行对应匹配，无需任何标注数据即可进行网络训练或微调。我们的方法利用极线约束指导匹配过程，并引入级联结构实现匹配的渐进式精化。通过综合实验，我们对E3CM的性能进行了全面评估，并证明其优于现有方法。为促进进一步研究并便于结果复现，我们在https://mias.group/E3CM 公开发布了源代码。