A Sequential Color Correction Approach for Texture Mapping of 3D Meshes
Author:
Dal’Col LucasORCID, Coelho DanielORCID, Madeira TiagoORCID, Dias PauloORCID, Oliveira MiguelORCID
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.
Funder
project I&DT nº 45382
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference67 articles.
1. Pérez, L., Rodríguez, Í., Rodríguez, N., Usamentiaga, R., and García, D.F. (2016). Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review. Sensors, 16. 2. Zhang, Y., Chen, H., Waslander, S.L., Yang, T., Zhang, S., Xiong, G., and Liu, K. (2018). Toward a More Complete, Flexible, and Safer Speed Planning for Autonomous Driving via Convex Optimization. Sensors, 18. 3. Ma, X., Wang, Z., Li, H., Zhang, P., Ouyang, W., and Fan, X. (November, January 27). Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, Republic of Korea. 4. Di Angelo, L., Di Stefano, P., Guardiani, E., Morabito, A.E., and Pane, C. (2019). 3D Virtual Reconstruction of the Ancient Roman Incile of the Fucino Lake. Sensors, 19. 5. Vázquez-Arellano, M., Griepentrog, H.W., Reiser, D., and Paraforos, D.S. (2016). 3-D Imaging Systems for Agricultural Applications—A Review. Sensors, 16.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|