Efficient global color correction for large-scale multiple-view images in three-dimensional reconstruction

Abstract

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.