Ultra-efficient 3D shape reconstruction: Line-coded absolute phase unwrapping algorithm

IF 7.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Haihua An , Yiping Cao , Hechen Zhang
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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.
超高效三维形状重建:线编码绝对相位展开算法
基于绝对相位解包裹的条纹投影轮廓术(APU-FPP)具有逐像素计算、高精度、全方位感知三维形状信息等优点。据我们所知,现有的APU-FPP方法由于投射额外的辅助编码条纹(ACFs)而存在精度和效率之间的矛盾。针对具有非均匀反射率和复杂曲面的场景,提出了一种线编码绝对相位展开(LCAPU)算法。首先,将单像素线序列依次嵌入到两组3步移相模式中,标记条纹周期,可以彻底避免额外的ACFs干扰相邻形态信息的一致性。其次,采用两种相移相同的线编码相移模式,识别出包含条纹顺序提示的对应编码线,并同时用于引导条纹相互补偿,从而提取出高质量的相位;最后,根据解码线的像素位置和条纹指数,提出一种多层解码算法,迭代生成适应形态变化随机性的条纹顺序图。与其他方法相比,所提出的LCAPU不仅可以实现单次图像采集的一次性三维形状重建,而且可以自动校正相位误差,平衡了超高效率和高精度。实验结果证明了该算法的优越性能和在动态复杂场景中的实际应用潜力。
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
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.
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