![]() Color correction approaches, in a general sense, can be divided into two main classes: model-based parametric and model-less non-parametric. There are several color correction approaches in the literature, however, the majority of them focus on correcting a pair of images, whereas our objective is to increase the level of similarity of multiple images. In other words, color correction consists of transferring the color palette of a reference image, usually called source image ( S), to a target image ( T). Color correction can be defined as the general problem of compensating the photometrical disparities between two coarsely geometrically registered images. For example, in authors propose to use Poisson image editing techniques to smooth the texture. This may be carried out using a form of post-processing operation. The seams are more or less visible depending on the similarity of colors in the images, which is why a proper color correction of the images is crucial to achieve seamless texture mapping. However, particularly in regions where the borders of the selected images meet, visual seam artifacts are often noticeable. These approaches address the problem of blurring and ghost artifacts. Others have used graph optimization mechanisms, or optimization procedures that minimize discontinuities between neighbouring faces. Some authors propose to employ a Markov random field to select the images. Still, selecting a single image to be used for texture mapping raises the problem of choosing the most adequate one from a discrete set of possibilities and additionally, how to produce a consistent selection of images for all the faces in the 3D mesh. ![]() ![]() However, these approaches are highly sensitive to inaccuracies in camera pose estimation, as even slight misalignments may generate ghost and blurring artifacts in the textures, which are not visually appealing. ![]() Several authors have proposed methodologies to carry out the fusion, based on different forms of weighted average of the contributions of textures in the image space. The problem is generally solved in two different ways: by selecting a single image from the set of possible images, or by fusing the texture from all available images. This entails the existence of a mechanism to handle the redundant photometric information, and is often referred to as multi-view texture mapping. The overlap will often occur in irregular regions, which creates an intricate combination of dependencies. However, in more recent applications, texture mapping has been applied to cases where several overlapping images are available to colorize the mesh. Texture mapping is the colorization of a 3D mesh using a single image. It is a widely researched topic in areas such as computer graphics and computer vision, and has recently gained attention in others, namely robotics, autonomous driving, medical applications, cultural heritage and agriculture. The creation of 3D models from the captured shape and appearance of objects is known as 3D reconstruction. The proposed approach enhances the visual quality of textured meshes by eliminating most of the texture seams. These show that the proposed approach outperforms all others, both in qualitative and quantitative metrics. Results include a comparison with four other color correction approaches. We also present a texture mapping pipeline that receives uncorrected images, an untextured mesh, and point clouds as inputs, producing a final textured mesh and color corrected images as output. To accomplish this goal, we propose two contributions to the state-of-the-art of color correction: a pairwise-based methodology, capable of color correcting multiple images from the same scene the application of 3D information from the scene, namely meshes and point clouds, to build a filtering procedure, in order to produce a more reliable spatial registration between images, thereby increasing the robustness of the color correction procedure. The main goal of this work is to produce textured meshes free of texture seams through a process of color correcting all images of the scene. These artifacts can be more or less visible, depending on the color similarity between the used images. Texture mapping of 3D models using multiple images often results in textured meshes with unappealing visual artifacts known as texture seams.
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