Consistent global color correction across multiple-view images in three-dimensional (3D) reconstruction is an important and challenging problem. The present work addresses this issue by proposing a novel global color correction method for multi-view images based on a spline curve remapping function. In contrast to existing methods, we obtain a series of optimal functions by minimizing the variance in the color values of all observations of every sparse point generated by the Structure from Motion (SfM) technique. We also find that adding only simple constraints to the spline is required to prevent the loss of image contrast and gradient information. The robustness of the proposed method is ensured by the adoption of strong geometric constraints between multi-view images. Finally, the applicability of the method to large-scale multiple-view images is facilitated by proposing a parallelizable hierarchical image color correction strategy based on a tree structure. The performance of the proposed method is compared with the performances of existing state-of-the-art methods when applied to several challenging datasets. The results indicate that the notable flexibility of the spline curve, along with the proposed optimization process and hierarchical strategy, not only enable the proposed method to perform well with challenging datasets, but also provide high computational efficiency when working with large-scale image sets.

在三维（3D）重建中跨多视图图像进行一致的全局色彩校正是一个重要且具有挑战性的问题。本工作通过提出一种新的基于样条曲线重新映射功能的多视图图像全局色彩校正方法来解决此问题。与现有方法相比，我们通过最小化“运动结构（SfM）”技术生成的每个稀疏点的所有观测值的色值差异，获得了一系列最佳函数。我们还发现，仅需向样条线添加简单约束即可防止图像对比度和梯度信息丢失。通过在多视图图像之间采用强大的几何约束，可以确保所提出方法的鲁棒性。最后，通过提出基于树结构的可并行化的分层图像色彩校正策略，促进了该方法对大规模多视点图像的适用性。将所提出的方法的性能与应用于多个具有挑战性的数据集的现有技术水平的性能进行比较。结果表明，样条曲线的显着灵活性以及所提出的优化过程和分层策略，不仅使所提出的方法能够很好地处理具有挑战性的数据集，而且在处理大规模图像集时也具有很高的计算效率。将所提出的方法的性能与应用于多个具有挑战性的数据集的现有技术水平的性能进行比较。结果表明，样条曲线的显着灵活性以及所提出的优化过程和分层策略，不仅使所提出的方法能够很好地处理具有挑战性的数据集，而且在处理大规模图像集时也具有很高的计算效率。将所提出的方法的性能与应用于多个具有挑战性的数据集的现有技术水平的性能进行比较。结果表明，样条曲线的显着灵活性以及所提出的优化过程和分层策略，不仅使所提出的方法能够很好地处理具有挑战性的数据集，而且在处理大规模图像集时也具有很高的计算效率。