Global Color Consistency Correction for Large-Scale Images in 3-D Reconstruction

Global color consistency correction for multiview images in three-dimensional (3-D) reconstruction is an important problem. The color differences between the images will affect the result of dense matching, thereby reducing the geometric accuracy of the mesh model. Moreover, it will also affect the result of texture mapping, causing color differences in the textured model. The color correction method based on global optimization is mainly used to solve this problem. And existing methods usually use sparse matching points as the color correspondences, but the correction results are not accurate enough as a result of the sparsity of the points. Besides, their efficiency of solving large-scale images globally is low. This article proposes a novel color correction method to eliminate the color differences between large-scale multiview images effectively. The core idea of our method is to group images by graph partition algorithm, and then perform intragroup correction and intergroup correction in sequence. First, for each pair of images, we calculate the reliable matching regions around the sparse points as the color correspondences according to the local homography principle. Compared with sparse matching points, our strategy can achieve more accurate color correction results. Next, for large-scale images, we partition them into many groups. For each group of images, the correction parameters are solved to eliminate the color differences of the images included in the group. Finally, we eliminate the color differences between groups by intergroup correction to achieve overall color consistency. Experimental results on typical datasets demonstrate that the proposed method is better than the current representative methods. The proposed method shows better color consistency in the extreme cases, and also exhibits higher computational efficiency on large-scale image sets.