A Sequential Color Correction Approach for Texture Mapping of 3D Meshes

Abstract

Texture mapping can be defined as the colorization of a 3D mesh using one or multiple images. In the case of multiple images, this process often results in textured meshes with unappealing visual artifacts, known as texture seams, caused by the lack of color similarity between the images. The main goal of this work is to create textured meshes free of texture seams by color correcting all the images used. We propose a novel color-correction approach, called sequential pairwise color correction, capable of color correcting multiple images from the same scene, using a pairwise-based method. This approach consists of sequentially color correcting each image of the set with respect to a reference image, following color-correction paths computed from a weighted graph. The color-correction algorithm is integrated with a texture-mapping pipeline that receives uncorrected images, a 3D mesh, and point clouds as inputs, producing color-corrected images and a textured mesh as outputs. Results show that the proposed approach outperforms several state-of-the-art color-correction algorithms, both in qualitative and quantitative evaluations. The approach eliminates most texture seams, significantly increasing the visual quality of the textured meshes.

Neural Colour Correction for Indoor 3D Reconstruction Using RGB-D Data

“…The reference image is decided by selecting the node with the largest centrality. We then compute the propagation paths by applying the shortest path algorithm, as suggested in [22,23].…”

“…Chen et al [22] defined both the propagation path and reference image by minimizing the normalized error using the shortest distance algorithm. Dal'Col et al [23] used the reprojection of geometric data to calculate accurate mappings between images and applied this information both on the computation of the shortest propagation path and in the calculation of the JIH colour mapping function. In these approaches, cumulative errors in the colour propagation can cause significant degradation of the results, particularly if there are pairs of images with low or incorrect mappings between them.…”