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Bas-relief modeling from RGB monocular images with regional division characteristics
Traditional Bas-relief modeling methods are often limited to inefficient and difficult to be altered after the product is formed. This paper presents a novel method for bas-relief modeling from RGB monocular images with regional division characteristics. The problem discussed in this paper involves...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755244/ https://www.ncbi.nlm.nih.gov/pubmed/36522358 http://dx.doi.org/10.1038/s41598-022-24974-0 |
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author | Wu, Xinli Zhao, Yun Luo, Jiali Zhang, Minxiong Yang, Wenzhen |
author_facet | Wu, Xinli Zhao, Yun Luo, Jiali Zhang, Minxiong Yang, Wenzhen |
author_sort | Wu, Xinli |
collection | PubMed |
description | Traditional Bas-relief modeling methods are often limited to inefficient and difficult to be altered after the product is formed. This paper presents a novel method for bas-relief modeling from RGB monocular images with regional division characteristics. The problem discussed in this paper involves edge detection, region division, height value recovery and three-dimensional reconstruction of image. In our framework, we can automatically obtain the pixel height of each area in the image and can adjust the concave–convex relationship of each image area to obtain a bas-relief modeling which can be printed directly in 3D. The edge detection algorithm used Gaussian difference pyramid to combine the luminance information and chrominance information of digital image; the regions of the RGB monocular image are divided by the improved connected-component labeling algorithm;and the 3D pixel point cloud of each region is calculated by the shape-from-shading algorithm. Different from previous work, our method can fully obtain the image height field data and completely restored the depth information,which makes it possible to use any RGB monocular image for bas-relief modeling. Experiments with groups of images show that our method can effectively generate bas-relief modeling. |
format | Online Article Text |
id | pubmed-9755244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97552442022-12-17 Bas-relief modeling from RGB monocular images with regional division characteristics Wu, Xinli Zhao, Yun Luo, Jiali Zhang, Minxiong Yang, Wenzhen Sci Rep Article Traditional Bas-relief modeling methods are often limited to inefficient and difficult to be altered after the product is formed. This paper presents a novel method for bas-relief modeling from RGB monocular images with regional division characteristics. The problem discussed in this paper involves edge detection, region division, height value recovery and three-dimensional reconstruction of image. In our framework, we can automatically obtain the pixel height of each area in the image and can adjust the concave–convex relationship of each image area to obtain a bas-relief modeling which can be printed directly in 3D. The edge detection algorithm used Gaussian difference pyramid to combine the luminance information and chrominance information of digital image; the regions of the RGB monocular image are divided by the improved connected-component labeling algorithm;and the 3D pixel point cloud of each region is calculated by the shape-from-shading algorithm. Different from previous work, our method can fully obtain the image height field data and completely restored the depth information,which makes it possible to use any RGB monocular image for bas-relief modeling. Experiments with groups of images show that our method can effectively generate bas-relief modeling. Nature Publishing Group UK 2022-12-15 /pmc/articles/PMC9755244/ /pubmed/36522358 http://dx.doi.org/10.1038/s41598-022-24974-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wu, Xinli Zhao, Yun Luo, Jiali Zhang, Minxiong Yang, Wenzhen Bas-relief modeling from RGB monocular images with regional division characteristics |
title | Bas-relief modeling from RGB monocular images with regional division characteristics |
title_full | Bas-relief modeling from RGB monocular images with regional division characteristics |
title_fullStr | Bas-relief modeling from RGB monocular images with regional division characteristics |
title_full_unstemmed | Bas-relief modeling from RGB monocular images with regional division characteristics |
title_short | Bas-relief modeling from RGB monocular images with regional division characteristics |
title_sort | bas-relief modeling from rgb monocular images with regional division characteristics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755244/ https://www.ncbi.nlm.nih.gov/pubmed/36522358 http://dx.doi.org/10.1038/s41598-022-24974-0 |
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