Robust matching, especially the number, precision and distribution of feature point matching, directly affects the effect of 3D reconstruction. However, the existing methods rarely consider these three aspects comprehensively to improve the quality of feature matching, which in turn affects the effect of 3D reconstruction. Therefore, to effectively improve the quality of 3D reconstruction, we propose a circle-based enhanced motion consistency and guided diffusion feature matching algorithm for 3D reconstruction named EMC+GD_C. Firstly, a circle-based neighborhood division method is proposed, which increases the number of initial matching points. Secondly, to improve the precision of feature point matching, on the one hand, we put forward the idea of enhancing motion consistency, reducing the mismatch of high similarity feature points by enhancing the judgment conditions of true and false matching points; on the other hand, we combine the RANSAC optimization method to filter out the outliers and further improve the precision of feature point matching. Finally, a novel guided diffusion idea combining guided matching and motion consistency is proposed, which expands the distribution range of feature point matching and improves the stability of 3D models. Experiments on 8 sets of 908 pairs of images in the public 3D reconstruction datasets demonstrate that our method can achieve better matching performance and show stronger stability in 3D reconstruction. Specifically, EMC+GD_C achieves an average improvement of 24.07% compared to SIFT-based ratio test, 9.18% to GMS and 1.94% to EMC+GD_G in feature matching precision.

鲁棒匹配，尤其是特征点匹配的数量、精度和分布，直接影响3D重建的效果。然而，现有方法很少综合考虑这三个方面来提高特征匹配的质量，进而影响3D重建的效果。因此，为了有效提高3D重建的质量，我们提出了一种基于圆的增强运动一致性和引导扩散特征匹配的3D重建算法EMC+GD_C。首先，提出了基于圆的邻域划分方法，增加了初始匹配点的数量。其次，为了提高特征点匹配的精度，一方面提出了增强运动一致性的思路，通过增强真假匹配点的判断条件来减少高相似度特征点的误匹配；另一方面，结合RANSAC优化方法过滤掉异常值，进一步提高特征点匹配的精度。最后，提出了一种结合引导匹配和运动一致性的新型引导扩散思想，扩大了特征点匹配的分布范围，提高了3D模型的稳定性。在公共3D重建数据集中的8组908对图像上进行的实验表明，我们的方法可以实现更好的匹配性能，并在3D重建中表现出更强的稳定性。具体来说，与基于SIFT的比率测试相比，EMC+GD_C在特征匹配精度方面平均提高了24.07%，比GMS平均提高了9.18%，比EMC+GD_G平均提高了1.94%。