Abstract

Texture mapping of 3D models using multiple images often results in textured meshes with unappealing visual artifacts known as texture seams. These artifacts can be more or less visible, depending on the color similarity between the used images. 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. 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. 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. Results include a comparison with four other color correction approaches. These show that the proposed approach outperforms all others, both in qualitative and quantitative metrics. The proposed approach enhances the visual quality of textured meshes by eliminating most of the texture seams.

Highlights

  • IntroductionSeveral 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 [16,17]

  • There are several color correction approaches in the literature, the majority of them focus on correcting a pair of images, whereas our objective is to increase the level of similarity of multiple images

  • The current paper proposes to estimate the Color Mapping Function (CMF) using a regression analysis to fit the observations from the Joint Image Histogram (JIH)

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Summary

Introduction

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 [16,17] 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 [18]. 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. This may be carried out using a form of post-processing operation. Color correction consists of transferring the color palette of a reference image, usually called source image (S), to a target image (T) [23]

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