融合几何约束提升图像匹配中的仿射对应精度
Learning Affine Correspondences by Integrating Geometric Constraints
- 结合密集匹配与新尺度方向估计算法提取仿射对应
- 在真实数据集上显著优于现有方法，提升姿态估计精度
- 适合需要高精度匹配与位姿估计的计算机视觉任务
仿射对应因其在图像匹配和位姿估计等任务中的优势受到广泛关注。现有方法在性能上仍存在诸多局限，因此探索新范式至关重要。本文提出一种新流程，通过融合密集匹配与几何约束来提取精确的仿射对应。具体而言，引入一种新型提取框架，结合密集匹配与新颖的关键点尺度和方向估计算法。为此，设计基于几何约束的损失函数，有效监督神经网络学习特征几何结构。实验表明，该方法在图像匹配任务中准确率和鲁棒性均优于现有方法。为进一步验证有效性，将其应用于相对位姿估计，在多个真实世界数据集上，由本方法提取的仿射对应能获得比基线更精确的位姿结果。代码已公开于 https://github.com/stilcrad/DenseAffine。
原文摘要 · Abstract (English)
Affine correspondences have received significant attention due to their benefits in tasks like image matching and pose estimation. Existing methods for extracting affine correspondences still have many limitations in terms of performance; thus, exploring a new paradigm is crucial. In this paper, we present a new pipeline designed for extracting accurate affine correspondences by integrating dense matching and geometric constraints. Specifically, a novel extraction framework is introduced, with the aid of dense matching and a novel keypoint scale and orientation estimator. For this purpose, we propose loss functions based on geometric constraints, which can effectively improve accuracy by supervising neural networks to learn feature geometry. The experimental show that the accuracy and robustness of our method outperform the existing ones in image matching tasks. To further demonstrate the effectiveness of the proposed method, we applied it to relative pose estimation. Affine correspondences extracted by our method lead to more accurate poses than the baselines on a range of real-world datasets. The code is available at https://github.com/stilcrad/DenseAffine.
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