平面轮廓的分段与识别技术

采用十一点法计算轮廓的近似曲率,该处比普通的三点法具有更强的抗噪性,提出利用曲率差分图进行特征点识别,并根据曲率差分图中轮廓特征点对应的波形特点,一次性将特征点识别为角点与切点,最后,对特征点间的线段类型加以识别,并进行连接调整和圆弧的分割与融合,从而较精确地实现了平面轮廓的分段及识别。     

一种间接提取轮廓特征点的算法

该文提出了一种间接提取轮廓特征点的新算法,该算法通过对轮廓点序列在不同方向进行分解以简化运算。文中给出了算法的原理、数学基础及实现过程。最后实验表明,该算法不仅计算量小,易于实现,对特征点的定位也很准确。     

基于轮廓的旋转和尺度不变区域的检测

为了获取鲁棒的特征区域,提出了一种基于轮廓的旋转和尺度不变区域的检测算法。算法应用多尺度乘积LoG(Laplacian of Gaussian)提取轮廓上稳定的角点作为特征点。根据角平分线的旋转和尺度不变性提取特征方向,利用特征方向求得特征半径。由角点、特征方向和特征半径构造不变区域。进行了特征匹配的实验,图像集包含旋转、尺度、仿射、光照和压缩五种变换,算法获得了很好的匹配结果。结果表明算法简单快速,具有较强的鲁棒性和广泛的应用性。     

一种基于形状约束势能的主动轮廓跟踪算法

将基于轮廓曲率的帧间几何形状约束势能,与目标区域信息和边缘梯度信息相结合,定义新的主动轮廓跟踪模型.该模型可以克服弱边缘及强背景等噪声对轮廓的吸引和干扰,同时保持目标的基本形状,实现和改善对具有尖角、深凹等不规则形状目标的边缘跟踪.采用基于块匹配的边界仿射变换方法对主动轮廓的初始位置进行估计,使其更接近目标的真实边缘.实验结果表明,该算法具有较好的边缘跟踪和抗复杂背景的能力.     

特征驱动的曲面重建

提出了基于散乱空间点集进行曲面重建的新方法,从点集的空间位置信息中提取待建曲面的内蕴特征量——法向和曲率,利用点集的这些特征信息来确定拓扑重建的搜索空间,采用面片生长的方式重建曲面。该方法在快速获得正确拓扑连接的同时,直接生成了用较少的面片就能保持曲面特征的优化网格。     

二维截面轮廓特征重建方法

针对目前轮廓特征识别算法存在识别结果易受噪声数据干扰、需对每一图形轮廓测量数据进行矢量化等问题,对二维截面轮廓特征重建方法进行研究,提出采用分步方式进行截面轮廓拟合的方法.首先,用平均增量法将测量数据进行直线段分段,并用最小二乘法对分段数据进行拟合,再把拟合结果存入链表内;然后,对于直线段链表内的相邻3条直线段,若相邻2条线段形成的半径满足给定阈值,则进行直线段圆弧拟合处理;最后,搜索拟合的圆弧段,如果存在2个或2个以上的连续圆弧,则将其重构为B样条曲线,得到二维截面轮廓特征的重构图形.该方法能有效减少拟合特征单元的数目,提高拟合精度.     

逆向工程中约束驱动数据点云曲面特征优化

为了获得产品原始设计意图,提高重构模型的整体质量,提出一种实用的逆向工程中约束驱动数据点云曲面特征优化方法,其中包括约束分解和有效的数值求解.在约束分解部分,通过设计结构矩阵分割算法消除几何约束系统中曲面特征间的耦合约束,提出了基于多尺度特征的凝聚算法来实现几何约束系统的简化和分解;在数值求解部分,基于罚函数法建立了约束优化的数学模型,采用BFGS法进行了数值求解.对优化后的逼近误差与约束满足误差进行分析的结果表明,采用文中方法可以低数量级的逼近误差的放大,实现约束满足误差的减小,获得一种全局优化的结果.     

基于平面路径的扫掠面高精度重建

从三角网格数据重建CAD建模过程是逆向工程中的研究重点之一,高效、高精的曲面重建具有重要的工程价值。针对由直线段和圆弧段组成的平面路径扫掠生成的三角网格表示曲面,提出了基于轮廓和路径曲线自动提取的扫掠过程重建,实现扫掠曲面的高精度重建。首先,基于统一三角网格模型曲率矢量场自动获得初始路径,再利用高斯映射迭代和配准拟合的方法生成了扫掠的轮廓曲线;然后,逆向计算扫掠路径的离散有序点集,通过引入切空间表示方法来识别路径中的直线段和圆弧段,并基于相切几何约束条件建立了拟合的优化模型,对初始路径进一步优化;最后,由计算得到的轮廓曲线和路径曲线执行扫掠操作,以获得重建的扫掠曲面。实验结果表明,该方法实现了自动提取轮廓曲线和路径曲线,进而重建扫掠模型的建模过程,减少了繁琐的人工交互,提取的轮廓和路径有效地避免了离散误差累积,使得最终重建的扫掠曲面精度更高,且适用于有噪声的数据和存在缺失数据的扫掠曲面。     

特征驱动的曲面重建*

提出了基于散乱空间点集进行曲面重建的新方法,从点集的空间位置信息中提取待建曲面的内蕴特征量——法向和曲率,利用点集的这些特征信息来确定拓扑重建的搜索空间,采用面片生长的方式重建曲面。该方法在快速获得正确拓扑连接的同时,直接生成了用较少的面片就能保持曲面特征的优化网格。     

A volumetric fusion technique for surface reconstruction from silhouettes and range data

Optical triangulation, an active reconstruction technique, is known to be an accurate method but has several shortcomings due to occlusion and laser reflectance properties of the object surface, that often lead to holes and inaccuracies on the recovered surface. Shape from silhouette, on the other hand, as a passive reconstruction technique, yields robust, hole-free reconstruction of the visual hull of the object. In this paper, a hybrid surface reconstruction method that fuses geometrical information acquired from silhouette images and optical triangulation is presented. Our motivation is to recover the geometry from silhouettes on those parts of the surface which the range data fail to capture. A volumetric octree representation is first obtained from the silhouette images and then carved by range points to amend the missing cavity information. An isolevel value on each surface cube of the carved octree structure is accumulated using local surface triangulations obtained separately from range data and silhouettes. The marching cubes algorithm is then applied for triangulation of the volumetric representation. The performance of the proposed technique is demonstrated on several real objects.     

The curvature interpolation method for surface reconstruction for geospatial point cloud data

ABSTRACT

Surface reconstruction for scattered data is an ill-posed problem and most computational algorithms become overly expensive as the number of sample points increases. This article studies an effective partial differential equation (PDE)-based algorithm, called the curvature interpolation method with iterative refinement (IR-CIM). The new method iteratively utilizes curvature-related information which is estimated from an intermediate surface of the nonuniform data and plays a role of driving force for the reconstruction of a reliable image surface. The IR-CIM is applied for digital elevation modelling for geospatial point cloud data of overlapping strip scans acquired by light detection and ranging (LiDAR) technology. This article also introduces an effective initialization strategy for large areas of missing data and a robust method for the elimination of the Moiré effect over strip overlaps. The resulting algorithm converges to a piecewise smooth image, with little dependence on sample rates, outperforming inverse-distance weighting methods in both efficiency and accuracy.     

Manifold surface reconstruction of an environment from sparse Structure-from-Motion data

The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-from-Motion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.     

Coarse-to-fine surface reconstruction from silhouettes and range data using mesh deformation

We present a coarse-to-fine surface reconstruction method based on mesh deformation to build watertight surface models of complex objects from their silhouettes and range data. The deformable mesh, which initially represents the object visual hull, is iteratively displaced towards the triangulated range surface using the line-of-sight information. Each iteration of the deformation algorithm involves smoothing and restructuring operations to regularize the surface evolution process. We define a non-shrinking and easy-to-compute smoothing operator that fairs the surface separately along its tangential and normal directions. The mesh restructuring operator, which is based on edge split, collapse and flip operations, enables the deformable mesh to adapt its shape to the object geometry without suffering from any geometrical distortions. By imposing appropriate minimum and maximum edge length constraints, the deformable mesh, hence the object surface, can be represented at increasing levels of detail. This coarse-to-fine strategy, that allows high resolution reconstructions even with deficient and irregularly sampled range data, not only provides robustness, but also significantly improves the computational efficiency of the deformation process. We demonstrate the performance of the proposed method on several real objects.     

Optimal structural similarity constraint for reversible data hiding

Until now, most reversible data hiding techniques have been evaluated by peak signal-to-noise ratio(PSNR), which based on mean squared error(MSE). Unfortunately, MSE turns out to be an extremely poor measure when the purpose is to predict perceived signal fidelity or quality. The structural similarity (SSIM) index has gained widespread popularity as an alternative motivating principle for the design of image quality measures. How to utilize the characterize of SSIM to design RDH algorithm is very critical. In this paper, we propose an optimal RDH algorithm under structural similarity constraint. Firstly, we deduce the metric of the structural similarity constraint, and further we prove it does’t hold non-crossing-edges property. Secondly, we construct the rate-distortion function of optimal structural similarity constraint, which is equivalent to minimize the average distortion for a given embedding rate, and then we can obtain the optimal transition probability matrix under the structural similarity constraint. Comparing with previous RDH, our method have gained the improvement of SSIM about 1.89 % on average. Experiments show that our proposed method outperforms the state-of-arts performance in SSIM.     

Implicit meshes for surface reconstruction

Deformable 3D models can be represented either as traditional explicit surfaces, such as triangulated meshes, or as implicit surfaces. Explicit surfaces are widely accepted because they are simple to deform and render, but fitting them involves minimizing a nondifferentiable distance function. By contrast, implicit surfaces allow fitting by minimizing a differentiate algebraic distance, but are harder to meaningfully deform and render. Here, we propose a method that combines the strength of both approaches. It relies on a technique that can turn a completely arbitrary triangulated mesh, such as one taken from the Web, into an implicit surface that closely approximates it and can deform in tandem with it. This allows both automated algorithms to take advantage of the attractive properties of implicit surfaces for fitting purposes and people to use standard deformation tools they feel comfortable for interaction and animation purposes. We demonstrate the applicability of our technique to modeling the human upper-body, including face, neck, shoulders, and ears, from noisy stereo and silhouette data.     

Surface and normal ensembles for surface reconstruction

The majority of the existing techniques for surface reconstruction and the closely related problem of normal reconstruction are deterministic. Their main advantages are the speed and, given a reasonably good initial input, the high quality of the reconstructed surfaces. Nevertheless, their deterministic nature may hinder them from effectively handling incomplete data with noise and outliers. An ensemble is a statistical technique which can improve the performance of deterministic algorithms by putting them into a statistics based probabilistic setting. In this paper, we study the suitability of ensembles in normal and surface reconstruction. We experimented with a widely used normal reconstruction technique [Hoppe H, DeRose T, Duchamp T, McDonald J, Stuetzle W. Surface reconstruction from unorganized points. Computer Graphics 1992;71-8] and Multi-level Partitions of Unity implicits for surface reconstruction [Ohtake Y, Belyaev A, Alexa M, Turk G, Seidel H-P. Multi-level partition of unity implicits. ACM Transactions on Graphics 2003;22(3):463-70], showing that normal and surface ensembles can successfully be combined to handle noisy point sets.     

Optimization-based approach for curve and surface reconstruction

This paper introduces an optimization-based approach for the curve reconstruction problem, where piecewise linear approximations are computed from sets of points sampled from target curves. In this approach, the problem is formulated as an optimization problem. To be more concrete, at first the Delaunay triangulation for the sample points is computed, and a weight is assigned with each Delaunay edge. Then the problem becomes minimization or maximization of the total weights of the edges that constitute the reconstruction. This paper proposes one exact method and two approximate methods, and shows that the obtained results are improved both theoretically and empirically. In addition, the optimization-based approach is further extended to three dimensions, where surfaces are to be reconstructed, and the quality of the reconstructions is examined.     

逆向工程中测量数据曲面重构技术研究

文中首先介绍了自由曲面的描述方式,并提出了一种对点云数据进行NURBS曲面重建的改进算法。该算法通过对目标函数的修正及迭代处理,将测点数据的有理B样条曲线拟合问题转化为一单变量的优化问题。从而容易求得控制点三维坐标、权值、各测点所对应的参数值及最优控制点的数目,进而有效地压缩测量点云数据量。该方法克服了现有方法存在的需要求解多变量的非线性优化问题,不能对最优控制点数目进行求取等缺陷。实践表明,文中提出的方法是行之有效的。     

Higher-order nonlinear priors for surface reconstruction

For surface reconstruction problems with noisy and incomplete range data, a Bayesian estimation approach can improve the overall quality of the surfaces. The Bayesian approach to surface estimation relies on a likelihood term, which ties the surface estimate to the input data, and the prior, which ensures surface smoothness or continuity. This paper introduces a new high-order, nonlinear prior for surface reconstruction. The proposed prior can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion. An exact solution would require solving a fourth-order partial differential equation (PDE), which can be difficult with conventional numerical techniques. Our approach is to solve a cascade system of two second-order PDEs, which resembles the original fourth-order system. This strategy is based on the observation that the generalization of image processing to surfaces entails filtering the surface normals. We solve one PDE for processing the normals and one for refitting the surface to the normals. Furthermore, we implement the associated surface deformations using level sets. Hence, the algorithm can accommodate very complex shapes with arbitrary and changing topologies. This paper gives the mathematical formulation and describes the numerical algorithms. We also show results using range and medical data.