{"title":"Ultra-efficient 3D shape reconstruction: Line-coded absolute phase unwrapping algorithm","authors":"Haihua An , Yiping Cao , Hechen Zhang","doi":"10.1016/j.patcog.2025.112366","DOIUrl":null,"url":null,"abstract":"<div><div>Absolute phase unwrapping-based fringe projection profilometry (APU-FPP) has the advantages of pixel-wise calculation, high precision, and full-field sensing of 3D shape information. To the best of our knowledge, existing APU-FPP methods have a general contradiction between accuracy and efficiency because of projecting extra auxiliary coded fringes (ACFs). In this paper, a line-coded absolute phase unwrapping (LCAPU) algorithm is presented for absolute 3D shape reconstruction of the scene with non-uniform reflectivity and complex surfaces. Firstly, a sequence of single-pixel lines is successively embedded into two sets of 3-step phase-shifting patterns to mark fringe periods, which can thoroughly avoid extra ACFs to disrupt the coherence of adjacent morphological information. Secondly, two line-coded phase-shifting patterns with the same phase shift are used to recognize the corresponding coded lines containing the fringe order cue, which can be simultaneously used to guide fringe mutual compensation, thereby extracting a high-quality phase. Finally, according to the pixel positions and the fringe indices of the decoded lines, a multi-layer decoding (MLD) algorithm is developed to iteratively generate a fringe order map, which can adapt to the randomness of morphological changes. Compared to other methods, the proposed LCAPU can not only perform a one-shot 3D shape reconstruction with a single image acquisition, but also automatically correct phase errors, balancing ultra-efficiency and high accuracy. Experimental results demonstrate the superior performance and the practical application potential in dynamic complex scenes.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":"172 ","pages":"Article 112366"},"PeriodicalIF":7.6000,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320325010271","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Absolute phase unwrapping-based fringe projection profilometry (APU-FPP) has the advantages of pixel-wise calculation, high precision, and full-field sensing of 3D shape information. To the best of our knowledge, existing APU-FPP methods have a general contradiction between accuracy and efficiency because of projecting extra auxiliary coded fringes (ACFs). In this paper, a line-coded absolute phase unwrapping (LCAPU) algorithm is presented for absolute 3D shape reconstruction of the scene with non-uniform reflectivity and complex surfaces. Firstly, a sequence of single-pixel lines is successively embedded into two sets of 3-step phase-shifting patterns to mark fringe periods, which can thoroughly avoid extra ACFs to disrupt the coherence of adjacent morphological information. Secondly, two line-coded phase-shifting patterns with the same phase shift are used to recognize the corresponding coded lines containing the fringe order cue, which can be simultaneously used to guide fringe mutual compensation, thereby extracting a high-quality phase. Finally, according to the pixel positions and the fringe indices of the decoded lines, a multi-layer decoding (MLD) algorithm is developed to iteratively generate a fringe order map, which can adapt to the randomness of morphological changes. Compared to other methods, the proposed LCAPU can not only perform a one-shot 3D shape reconstruction with a single image acquisition, but also automatically correct phase errors, balancing ultra-efficiency and high accuracy. Experimental results demonstrate the superior performance and the practical application potential in dynamic complex scenes.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.