增强现实中的三维物体注册方法及其应用研究
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摘要
增强现实(Augmented Reality)是近年来的一个研究热点,有着广泛的应用前景。与传统的虚拟现实(Virtual Reality)不同,增强现实系统通过注册技术将计算机生成的虚拟信息显示在用户的视野中,从感官上将虚拟信息与真实环境无缝的融为一体,从而增强用户对现实世界的感知能力和交互能力。
虽然,平面物体的增强现实注册方法在学术界得到了广泛的研究,但是,三维物体的增强现实注册方法仍然存在很大的局限性。现有的基于计算机视觉的三维物体识别与注册方法普遍存在搜索空间大、计算时间长等问题。针对这些问题,本文的研究工作主要包含以下几个方面：
首先,本文针对无标志物三维物体识别与注册问题,提出了一种基于离散梯度特征的实时三维物体识别方法。该方法将三维物体的外观离散为一系列视图,从视图中提取若干包含丰富的梯度特征信息的子区域,并通过这些子区域组合起来,构建用于三维物体识别的组合模型。在物体识别过程中,将图像中梯度信息转化为离散梯度特征,采用旋转方向二进制编码技术与基于SSE 4.2 CPU指令集的并行运算技术,极大的提高了物体识别的运算效率,从而将运算量巨大的基于视图的三维物体识别方法的执行效率提高到增强现实系统的实时性要求水平(帧率≥15审s)。接着,采用基于机器学习的FAST算法提取图像中的特征点,并通过先进的BRIEF算子对特征点进行描述与匹配。
然后,本文针对增强现实注册的核心问题摄像头位姿计算,研究了透视三点问题(Perspective 3 Point Problem,简称P3P)与透视n点问题(Perspective n Point Problem,简称PnP),提出了新的理论与方法,极大的提高了摄像头位姿求解算法的实时性与稳定性。(1)针对透视三点问题,提出了一种基于透视相似三角形几何约束的P3P直接解法,减少了方程组中未知数的数量,降低方程复杂度,显著提高了解的数值稳定性与精确性。(2)研究了透视n点问题的2D/3D匹配点对的配置问题,发现了一种新的中间状态“准奇异情况”；提出了一种稳定高效的RPnP算法,有效的解决了准奇异情况下的稳定性退化问题；RPnP是第一个可以在缺乏冗余参考点的情况下(n≤5)获得比迭代算法更精确的解非迭代PnP算法,而且运算效率高,可以高效的处理大量的点集。
接着,本文针对基于标志物的三维物体注册的实时性问题,提出了一种基于查表(Lookup Table,简称LUT)的标志物注册算法,该算法可以在噪声情况下获得很高的稳定性,同时只需要很少的计算量,尤其适合计算资源有限的移动设备增强现实应用。
最后,本文针对虚实融合环境下的产品装配应用,我们开发了增强现实装配原型系统,通过增强信息指引用户进行正确的装配操作。该原型系统对本文提出的增强现实三维物体注册方法的有效性进行了验证。
Augmented Reality (AR) technology is a hotspot which has a wide range of applications and broad prospects. Being different from the traditional virtual reality (VR) technology, AR augments the real environment with the virtual contents generated by the computer to enhance the user's perception. The virtual contents and the real environment are seamlessly integrated, and the communication interface between the user and real world is enhanced.
In augmented reality, comparing with the registration method for planar target, the 3D object registration technology has the problems such as huge searching space and long computational time. Aiming at these problems, the research works of the thesis are as follows:
Firstly, aiming at the mark-less 3D object recognition and registration problem, a real-time object recognition algorithm based on discrete gradient feature is presented. The aspects of the object are recorded into a series of views, and each view is divided into several information rich sub-regions. The recognition is performed by using the discrete gradient feature. The computational efficiency of the view-based 3D object recognition task is significantly enhanced by the binary encoding and the parallel computing technology, which can meet the requirement of the AR frame rate (≥15fps). And then, a machine learning based FAST algorithm is used to extract the feature points, and the BRIEF operator is used to descript and to match the feature points.
Secondly, theories and methods are proposed for the stability and efficiency of the camera pose estimation process in the AR registration. (1) A direct solution of Perspective-3-Point problem based on a new geometric constraint named Perspective Similar Triangular (PST) is presented. By constructing the new geometric constraint, the number of the unknown variables of the equation system was reduced, which significantly enhanced the numerical stability and the accuracy of the solution. (2) The configuration of the 2D/3D corresponding point pairs in Perspective-n-Point problem is studied, and a new middle state, we called "quasi-singular", is proposed. A high robust algorithm RPnP is designed to resolve the stability degeneration problem in the quasi-singular case. RPnP is the first non-iterative solution for the PnP problem and can achieve more accurate results than the iterative algorithms when no redundant reference points can be used (n≤5), and large-size point set can be handled efficiently due to the O(n) computational complexity.
Thirdly, an efficient lookup table (LUT)-based camera pose estimation method is presented. It can achieve high stability in the presence of noise with very little computational time, and it is very suitable for the AR applications on mobile equipments with limited computational resources.
Finally, aiming at the product assembly application in the virtual-real environment, an AR assembly prototype system is developed. The user is guided by the augmented information to perform the correct assembly operations. The effectiveness of the 3D object registration method in AR is validated by this prototype system.
虽然,平面物体的增强现实注册方法在学术界得到了广泛的研究,但是,三维物体的增强现实注册方法仍然存在很大的局限性。现有的基于计算机视觉的三维物体识别与注册方法普遍存在搜索空间大、计算时间长等问题。针对这些问题,本文的研究工作主要包含以下几个方面：
首先,本文针对无标志物三维物体识别与注册问题,提出了一种基于离散梯度特征的实时三维物体识别方法。该方法将三维物体的外观离散为一系列视图,从视图中提取若干包含丰富的梯度特征信息的子区域,并通过这些子区域组合起来,构建用于三维物体识别的组合模型。在物体识别过程中,将图像中梯度信息转化为离散梯度特征,采用旋转方向二进制编码技术与基于SSE 4.2 CPU指令集的并行运算技术,极大的提高了物体识别的运算效率,从而将运算量巨大的基于视图的三维物体识别方法的执行效率提高到增强现实系统的实时性要求水平(帧率≥15审s)。接着,采用基于机器学习的FAST算法提取图像中的特征点,并通过先进的BRIEF算子对特征点进行描述与匹配。
然后,本文针对增强现实注册的核心问题摄像头位姿计算,研究了透视三点问题(Perspective 3 Point Problem,简称P3P)与透视n点问题(Perspective n Point Problem,简称PnP),提出了新的理论与方法,极大的提高了摄像头位姿求解算法的实时性与稳定性。(1)针对透视三点问题,提出了一种基于透视相似三角形几何约束的P3P直接解法,减少了方程组中未知数的数量,降低方程复杂度,显著提高了解的数值稳定性与精确性。(2)研究了透视n点问题的2D/3D匹配点对的配置问题,发现了一种新的中间状态“准奇异情况”；提出了一种稳定高效的RPnP算法,有效的解决了准奇异情况下的稳定性退化问题；RPnP是第一个可以在缺乏冗余参考点的情况下(n≤5)获得比迭代算法更精确的解非迭代PnP算法,而且运算效率高,可以高效的处理大量的点集。
接着,本文针对基于标志物的三维物体注册的实时性问题,提出了一种基于查表(Lookup Table,简称LUT)的标志物注册算法,该算法可以在噪声情况下获得很高的稳定性,同时只需要很少的计算量,尤其适合计算资源有限的移动设备增强现实应用。
最后,本文针对虚实融合环境下的产品装配应用,我们开发了增强现实装配原型系统,通过增强信息指引用户进行正确的装配操作。该原型系统对本文提出的增强现实三维物体注册方法的有效性进行了验证。
Augmented Reality (AR) technology is a hotspot which has a wide range of applications and broad prospects. Being different from the traditional virtual reality (VR) technology, AR augments the real environment with the virtual contents generated by the computer to enhance the user's perception. The virtual contents and the real environment are seamlessly integrated, and the communication interface between the user and real world is enhanced.
In augmented reality, comparing with the registration method for planar target, the 3D object registration technology has the problems such as huge searching space and long computational time. Aiming at these problems, the research works of the thesis are as follows:
Firstly, aiming at the mark-less 3D object recognition and registration problem, a real-time object recognition algorithm based on discrete gradient feature is presented. The aspects of the object are recorded into a series of views, and each view is divided into several information rich sub-regions. The recognition is performed by using the discrete gradient feature. The computational efficiency of the view-based 3D object recognition task is significantly enhanced by the binary encoding and the parallel computing technology, which can meet the requirement of the AR frame rate (≥15fps). And then, a machine learning based FAST algorithm is used to extract the feature points, and the BRIEF operator is used to descript and to match the feature points.
Secondly, theories and methods are proposed for the stability and efficiency of the camera pose estimation process in the AR registration. (1) A direct solution of Perspective-3-Point problem based on a new geometric constraint named Perspective Similar Triangular (PST) is presented. By constructing the new geometric constraint, the number of the unknown variables of the equation system was reduced, which significantly enhanced the numerical stability and the accuracy of the solution. (2) The configuration of the 2D/3D corresponding point pairs in Perspective-n-Point problem is studied, and a new middle state, we called "quasi-singular", is proposed. A high robust algorithm RPnP is designed to resolve the stability degeneration problem in the quasi-singular case. RPnP is the first non-iterative solution for the PnP problem and can achieve more accurate results than the iterative algorithms when no redundant reference points can be used (n≤5), and large-size point set can be handled efficiently due to the O(n) computational complexity.
Thirdly, an efficient lookup table (LUT)-based camera pose estimation method is presented. It can achieve high stability in the presence of noise with very little computational time, and it is very suitable for the AR applications on mobile equipments with limited computational resources.
Finally, aiming at the product assembly application in the virtual-real environment, an AR assembly prototype system is developed. The user is guided by the augmented information to perform the correct assembly operations. The effectiveness of the 3D object registration method in AR is validated by this prototype system.
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