Modeling-Based Decoding of the Point Pattern for Active Stereo Vision

IF 6.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Automation Science and Engineering Pub Date : 2024-07-29 DOI:10.1109/TASE.2024.3422424
ZhenZhou Wang;YongCan Shuang
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Abstract

Active stereo vision (ASV) computes the parallax and depth information from the coded structured light patterns. Thus, it could overcome the difficulties of measuring objects without textures and colors. Pattern decoding is used to obtain the indexed points with pixel coordinates from the acquired pattern image for the subsequent stereo matching. However, decoding of the structured light patterns at locations of color crosstalk, specular reflection and occlusion remains challenging. In this paper, we propose a pattern modeling method to decode the structured light point pattern robustly. The mapping model between the points in the designed pattern and the points in the segmented pattern is computed based on the natural neighbor interpolation. Based on the modeled points, the missing points in the segmented pattern are restored and the noise points in the segmented pattern are removed. The robustness is achieved in the sense of hundred percent point segmentation completeness. Due to the hundred percent completeness, the points in the corresponding blocks are matched directly according to their indexes. Experimental results verified the effectiveness of the proposed active stereo vision approach and also proved that the proposed pattern modeling method could solve the segmentation problems caused by specular reflection, color crosstalk and occlusion effectively. Note to Practitioners—The proposed approach tries to solve the segmentation problems caused by color crosstalk, specular reflection and occlusions for exiting point-pattern based 3D surface imaging techniques. The proposed approach utilizes the natural neighbor interpolation technique to compute the mapping model between the designed points and the segmented points robustly. Based on the mapping model, the missing points could be restored and the noise points could be removed. As a result, the robust segmentation results with all the projected points could be obtained. The segmentation problems caused by specular reflection, color crosstalk and occlusions are thus solved successfully. Although the proposed approach is developed based on our designed point pattern, it could also be easily expanded to other structured light point patterns. Currently, the proposed approach only works for the point patterns and much more work is still required before it could be applied to other types of structured light patterns, such as the line patterns or the phase patterns.
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基于建模的主动立体视觉点模式解码
主动立体视觉(ASV)从编码的结构光模式中计算视差和深度信息。因此,它可以克服测量物体没有纹理和颜色的困难。模式解码用于从获取的模式图像中获得具有像素坐标的索引点,用于后续的立体匹配。然而,解码彩色串扰、镜面反射和遮挡位置的结构光模式仍然具有挑战性。本文提出了一种模式建模方法对结构光点模式进行鲁棒解码。基于自然邻域插值,计算出设计图形中点与分割图形中点之间的映射模型。在建模点的基础上,对分割模式中的缺失点进行恢复,并去除分割模式中的噪声点。鲁棒性是在100%的点分割完整性的意义上实现的。由于100%的完整性,相应块中的点直接根据其索引进行匹配。实验结果验证了所提出的主动立体视觉方法的有效性,也证明了所提出的模式建模方法可以有效地解决镜面反射、颜色串扰和遮挡引起的分割问题。从业人员注意:本文提出的方法试图解决现有基于点模式的三维表面成像技术中由颜色串扰、镜面反射和遮挡引起的分割问题。该方法利用自然邻域插值技术,鲁棒地计算设计点与分割点之间的映射模型。基于该映射模型,可以对缺失点进行恢复,对噪声点进行去除。结果表明,所有投影点都能得到鲁棒的分割结果。成功地解决了镜面反射、颜色串扰和遮挡引起的分割问题。虽然提出的方法是基于我们设计的点模式,但它也可以很容易地扩展到其他结构光点模式。目前,所提出的方法只适用于点模式,在将其应用于其他类型的结构光模式(如线模式或相位模式)之前,还需要做更多的工作。
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来源期刊
IEEE Transactions on Automation Science and Engineering
IEEE Transactions on Automation Science and Engineering 工程技术-自动化与控制系统
CiteScore
12.50
自引率
14.30%
发文量
404
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Automation Science and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. T-ASE welcomes results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, security, service, supply chains, and transportation. T-ASE addresses a research community willing to integrate knowledge across disciplines and industries. For this purpose, each paper includes a Note to Practitioners that summarizes how its results can be applied or how they might be extended to apply in practice.
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