三维点云的鲁棒处理技术研究
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摘要
基于各种获取设备生成真实逼真的三维模型是获得物体几何信息的主要手段之一,在计算机图形学、计算机视觉、考古学、测绘学等领域得到广泛应用。获取设备得到的原始数据通常以三维点云表示,即同一空间参考系下表达物体空间分布和表面特性的三维点集合。虽然由点云重建物体表面以及相关的处理技术已有多年的研究历史并取得了大量的研究成果,但由于各个领域对获取技术的需求不断增加,以及已有点云处理技术在各种应用中表现出的不足,尤其是处理点云中由获取设备本身的物理限制以及获取条件和过程产生的各种缺陷,如噪声、外点、数据缺失、尖锐特征的丢失和不均匀采样等所表现出的鲁棒性不足,许多研究机构和研究人员一直从事于点云鲁棒处理中各种关键技术的研究与改进,以提高所获取三维数据的质量,从而促进三维获取技术在各个领域的应用与普及。为此,本文对点云鲁棒处理中的主要关键技术进行了深入研究,主要包括特征保持的点云增强、鲁棒的法向量估计、点云特征线提取和使用简单形状基元对点云表面进行全局最优拟合等技术。本文工作的主要贡献和创新总结如下:
(1)为了在点云增强中消除噪声、外点和不均匀采样等缺陷的同时,保留能够刻画表面特性的尖锐特征,提出了一种新颖的特征保持的点云增强方法。使用法向量加权为已有的基于加权局部最优投影(WLOP)的增强方法赋予特征保持的能力,即借助法向量信息,为局部邻域中法向量相似的相邻点赋予较大的权值,而将法向量差距较大的点看作是外点,赋予较小的权值,从而使得新位置主要由位于特征同一侧的相邻点决定。同时,为了在改进点云位置分布的同时得到特征保持的法向量信息,本文提出将法向量磨光集成到新的增强方法中,以此得到精确且保持特征的法向量。在合成点云和实际获取点云上的实验结果表明,与原有WLOP方法相比,本文方法能够在降低噪声、消除外点以及改善分布密度的同时,保持点云中的尖锐特征。
(2)为了解决噪声、外点和尖锐特征给法向量估计带来的挑战,提出了一种鲁棒的点云法向量估计算法。借鉴鲁棒统计中的多结构检测思想,基于在每个点局部邻域内随机采样得到的一组平面,首先使用鲁棒的无偏估计为此邻域计算局部噪声大小,然后定义基于核密度估计的目标函数选择出最优切平面。同时,选择最优切平面的过程还可以有效地检测出点云中的外点。实验结果表明本文方法对点云中的噪声、外点和尖锐特征具有很高的鲁棒性,能够为点云估计出光滑且特征保持的法向量。与现有估计方法相比,本文方法能够实现对特征保持的点云法向量的直接估计,且不需要复杂的参数调节。
(3)为了在含有噪声、外点和数据缺失的点云中检测出反映其几何特征的特征线,提出了一种基于RANSAC的特征线提取算法。此算法针对由建筑物或机械部件等具有平面特征的物体扫描得到的点云,基于在点云中检测出的多个平面,首先将每个平面所能拟合的点投影至此平面,并以投影区域的边界点作为候选,提出一种考虑全局约束的RANSAC特征线提取算法检测出最终的特征线。实验结果表明该方法对点云中的噪声、外点和数据缺失具有较高的鲁棒性,能够准确地提取出特征线。与已有的基于局部分析的方法相比,本文方法具有更高的鲁棒性,因而能适用于前者无法处理的低质量点云。
(4)为了使用基本形状基元实现对点云表面的精确拟合并避免过拟合问题,从而利于拟合结果对点云表面结构的揭示,提出了一种将变分表面拟合与模型选择相结合的新颖拟合算法。在拟合中考虑由基元类型、基元数目以及拟合误差导致的拟合代价,将问题形式化为对输入点云表面的划分、所用形状基元的类型及基元数目的一个整体优化,并给出了此组合优化问题的近似解法。首先创建合适的候选冗余集,然后通过模型选择和变分表面拟合迭代地精化每个候选直至收敛,最后通过一个全局的模型选择确定最终的基元。实验结果表明,该方法能够最大化所选择基元的拟合能力来实现对输入点云的准确表示,同时对噪声也有很高的鲁棒性。与已有方法相比,本文方法能够自动确定最优的表面划分以及每个划分对应的基元类型,在拟合复杂度和拟合质量之间取得折衷,而不需要预先指定基元数目。
One of the common approaches of obtaining the geometry information of the objects in the world is generating realistic and vivid 3D models with the aid of various kinds of acquisition devices. This approach has been widely used in Computer Graphics, Computer Vision, Archaeology, Topography, etc. The raw output of acquisition devices is usually represented by Point Clouds, i.e. a set of 3D points in the same reference coordinate, which can describe the spacial distribution and surface properties of the object. Although techniques for surface reconstruction from point clouds as well as point clouds processing have been extensively studied and many advances have been made in the past years, many academies and researchers have been working on the development and improvement of the key techniques of robust point clouds processing, in the hope of improving the quality of the data and rendering the application as well as prevalence of the 3D acquisition techniques. There are two reasons for that, one of which is the increasing of the demand on acquisition techniques from different fields, and the other is the limitations exposed by existing point clouds processing techniques, especially the deficiency of robustness of these techniques when dealing with the defects contained in the data because of the limitations of acquisition devices, acquisition condition and acquisition procedures, such as noise, outliers, holes, missing of sharp features and uneven distribution. This thesis has studied the key techniques of the robust processing of point clouds, including feature preserving point clouds consolidation, robust normal estimation, feature lines extraction and global optimal approximation using simple shape primitives. In summary, this thesis makes the following contributions:
(1) A novel feature preserving consolidation method for point clouds is proposed to preserve the sharp features, which are capable of capturing the surface properties, while eliminating the defects in point clouds, including noise, outliers and uneven distribution during the consolidation. The proposed method enables the existing Weighted Locally Optimal Projection (WLOP) based consolidation to preserve features by using weight function defined on normals. With the help of normal information, points with similar normals in the local neighborhood will be assigned larger weights, while points with large normal deviations are regarded as outliers and get smaller weights. Thus the new position is mainly defined by neighbors from the same side of the feature. At the same time, a normal mollification step is added to the new consolidation method to get accurate as well as feature preserving normals while the distribution of the points is improved. Experimental results on synthetic and real-world scanned data show that compared with the original WLOP, the proposed method can achieve denoisng, outlier removal and better distribution density as well as feature preserving on the consolidated point clouds.
(2) A robust normal estimation method for point clouds is presented in order to deal with challenges from noise, outliers and sharp features. The idea of multiple structure detection in the field of robust statistics is applied in the proposed method. A random sampling approach is adopted to generate a set of candidate planes in the local neighborhood of each point. Based on these planes, a robust unbiased noise scale estimator is used to compute the local noise scale, then a Kernel Density Estimation (KDE) based objective function is defined to find the best tangent plane. At the same time, the procedure for tangent plane selection can also be used to detect outliers contained in the point clouds. Experimental results show that the proposed method is highly robust to noise, outliers and sharp features, thus can obtain smooth while feature preserving normals for the point clouds. Compared with existing methods, the proposed method is able to directly obtain feature preserving normals while avoiding tuning of complex parameters.
(3) A RANSAC based method is proposed to detect feature lines which can describe the geometry features of point clouds which usually contaminated by noise, outliers and data missing. The proposed method is specially designed for the point clouds scanned from buildings or mechanical parts which usually contain planar parts. Starting from many planes detected in the point clouds, the proposed method projects all the points onto the planes which can approximate them well. The points on the boundaries of the projected regions are used as candidates for the final features lines, which are detected by the proposed RANSAC-based line detection method with global constraints. Experimental results show that the proposed method is highly robust to noise, outliers and data missing in the point clouds, and can extract accurate feature lines. Compared with existing methods based on local analysis, the proposed method is more robust, thus can be applied to more point clouds with low quality which are difficult to other methods.
(4) A novel method for approximating point clouds with simple shape primitives is proposed to achieve accurate approximation while avoiding over-fitting, which can hinder the approximation from capturing the structures of point clouds. The proposed method combines variational surface approximation and model selection, and formulates the approximation as a global optimal problem on the partition of the surface of the given point cloud, the types of employed primitives as well as the number of the primitives by considering the cost of types and number of the primitives as well as the approximation error. Then an approximate solution for this combination optimization problem is given. A redundant set of candidate primitives is created, and then each candidate is iteratively refined using model selection and variational surface approximation until convergence. Finally a global model selection is used to determine employed primitives. Experimental results show that this method can generate accurate representation of the given surface that maximizes the approximation ability of the selected primitives, while being robust to noise. Compared with existing methods, the proposed one can automatically determine the optimal partition of the given point cloud as well as the type of primitive for each patch in the partition and achieve a trade-off between the complexity of approximation and the approximation quality while being independent on the specified number of primitives.
(1)为了在点云增强中消除噪声、外点和不均匀采样等缺陷的同时,保留能够刻画表面特性的尖锐特征,提出了一种新颖的特征保持的点云增强方法。使用法向量加权为已有的基于加权局部最优投影(WLOP)的增强方法赋予特征保持的能力,即借助法向量信息,为局部邻域中法向量相似的相邻点赋予较大的权值,而将法向量差距较大的点看作是外点,赋予较小的权值,从而使得新位置主要由位于特征同一侧的相邻点决定。同时,为了在改进点云位置分布的同时得到特征保持的法向量信息,本文提出将法向量磨光集成到新的增强方法中,以此得到精确且保持特征的法向量。在合成点云和实际获取点云上的实验结果表明,与原有WLOP方法相比,本文方法能够在降低噪声、消除外点以及改善分布密度的同时,保持点云中的尖锐特征。
(2)为了解决噪声、外点和尖锐特征给法向量估计带来的挑战,提出了一种鲁棒的点云法向量估计算法。借鉴鲁棒统计中的多结构检测思想,基于在每个点局部邻域内随机采样得到的一组平面,首先使用鲁棒的无偏估计为此邻域计算局部噪声大小,然后定义基于核密度估计的目标函数选择出最优切平面。同时,选择最优切平面的过程还可以有效地检测出点云中的外点。实验结果表明本文方法对点云中的噪声、外点和尖锐特征具有很高的鲁棒性,能够为点云估计出光滑且特征保持的法向量。与现有估计方法相比,本文方法能够实现对特征保持的点云法向量的直接估计,且不需要复杂的参数调节。
(3)为了在含有噪声、外点和数据缺失的点云中检测出反映其几何特征的特征线,提出了一种基于RANSAC的特征线提取算法。此算法针对由建筑物或机械部件等具有平面特征的物体扫描得到的点云,基于在点云中检测出的多个平面,首先将每个平面所能拟合的点投影至此平面,并以投影区域的边界点作为候选,提出一种考虑全局约束的RANSAC特征线提取算法检测出最终的特征线。实验结果表明该方法对点云中的噪声、外点和数据缺失具有较高的鲁棒性,能够准确地提取出特征线。与已有的基于局部分析的方法相比,本文方法具有更高的鲁棒性,因而能适用于前者无法处理的低质量点云。
(4)为了使用基本形状基元实现对点云表面的精确拟合并避免过拟合问题,从而利于拟合结果对点云表面结构的揭示,提出了一种将变分表面拟合与模型选择相结合的新颖拟合算法。在拟合中考虑由基元类型、基元数目以及拟合误差导致的拟合代价,将问题形式化为对输入点云表面的划分、所用形状基元的类型及基元数目的一个整体优化,并给出了此组合优化问题的近似解法。首先创建合适的候选冗余集,然后通过模型选择和变分表面拟合迭代地精化每个候选直至收敛,最后通过一个全局的模型选择确定最终的基元。实验结果表明,该方法能够最大化所选择基元的拟合能力来实现对输入点云的准确表示,同时对噪声也有很高的鲁棒性。与已有方法相比,本文方法能够自动确定最优的表面划分以及每个划分对应的基元类型,在拟合复杂度和拟合质量之间取得折衷,而不需要预先指定基元数目。
One of the common approaches of obtaining the geometry information of the objects in the world is generating realistic and vivid 3D models with the aid of various kinds of acquisition devices. This approach has been widely used in Computer Graphics, Computer Vision, Archaeology, Topography, etc. The raw output of acquisition devices is usually represented by Point Clouds, i.e. a set of 3D points in the same reference coordinate, which can describe the spacial distribution and surface properties of the object. Although techniques for surface reconstruction from point clouds as well as point clouds processing have been extensively studied and many advances have been made in the past years, many academies and researchers have been working on the development and improvement of the key techniques of robust point clouds processing, in the hope of improving the quality of the data and rendering the application as well as prevalence of the 3D acquisition techniques. There are two reasons for that, one of which is the increasing of the demand on acquisition techniques from different fields, and the other is the limitations exposed by existing point clouds processing techniques, especially the deficiency of robustness of these techniques when dealing with the defects contained in the data because of the limitations of acquisition devices, acquisition condition and acquisition procedures, such as noise, outliers, holes, missing of sharp features and uneven distribution. This thesis has studied the key techniques of the robust processing of point clouds, including feature preserving point clouds consolidation, robust normal estimation, feature lines extraction and global optimal approximation using simple shape primitives. In summary, this thesis makes the following contributions:
(1) A novel feature preserving consolidation method for point clouds is proposed to preserve the sharp features, which are capable of capturing the surface properties, while eliminating the defects in point clouds, including noise, outliers and uneven distribution during the consolidation. The proposed method enables the existing Weighted Locally Optimal Projection (WLOP) based consolidation to preserve features by using weight function defined on normals. With the help of normal information, points with similar normals in the local neighborhood will be assigned larger weights, while points with large normal deviations are regarded as outliers and get smaller weights. Thus the new position is mainly defined by neighbors from the same side of the feature. At the same time, a normal mollification step is added to the new consolidation method to get accurate as well as feature preserving normals while the distribution of the points is improved. Experimental results on synthetic and real-world scanned data show that compared with the original WLOP, the proposed method can achieve denoisng, outlier removal and better distribution density as well as feature preserving on the consolidated point clouds.
(2) A robust normal estimation method for point clouds is presented in order to deal with challenges from noise, outliers and sharp features. The idea of multiple structure detection in the field of robust statistics is applied in the proposed method. A random sampling approach is adopted to generate a set of candidate planes in the local neighborhood of each point. Based on these planes, a robust unbiased noise scale estimator is used to compute the local noise scale, then a Kernel Density Estimation (KDE) based objective function is defined to find the best tangent plane. At the same time, the procedure for tangent plane selection can also be used to detect outliers contained in the point clouds. Experimental results show that the proposed method is highly robust to noise, outliers and sharp features, thus can obtain smooth while feature preserving normals for the point clouds. Compared with existing methods, the proposed method is able to directly obtain feature preserving normals while avoiding tuning of complex parameters.
(3) A RANSAC based method is proposed to detect feature lines which can describe the geometry features of point clouds which usually contaminated by noise, outliers and data missing. The proposed method is specially designed for the point clouds scanned from buildings or mechanical parts which usually contain planar parts. Starting from many planes detected in the point clouds, the proposed method projects all the points onto the planes which can approximate them well. The points on the boundaries of the projected regions are used as candidates for the final features lines, which are detected by the proposed RANSAC-based line detection method with global constraints. Experimental results show that the proposed method is highly robust to noise, outliers and data missing in the point clouds, and can extract accurate feature lines. Compared with existing methods based on local analysis, the proposed method is more robust, thus can be applied to more point clouds with low quality which are difficult to other methods.
(4) A novel method for approximating point clouds with simple shape primitives is proposed to achieve accurate approximation while avoiding over-fitting, which can hinder the approximation from capturing the structures of point clouds. The proposed method combines variational surface approximation and model selection, and formulates the approximation as a global optimal problem on the partition of the surface of the given point cloud, the types of employed primitives as well as the number of the primitives by considering the cost of types and number of the primitives as well as the approximation error. Then an approximate solution for this combination optimization problem is given. A redundant set of candidate primitives is created, and then each candidate is iteratively refined using model selection and variational surface approximation until convergence. Finally a global model selection is used to determine employed primitives. Experimental results show that this method can generate accurate representation of the given surface that maximizes the approximation ability of the selected primitives, while being robust to noise. Compared with existing methods, the proposed one can automatically determine the optimal partition of the given point cloud as well as the type of primitive for each patch in the partition and achieve a trade-off between the complexity of approximation and the approximation quality while being independent on the specified number of primitives.
引文
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