Volumetric stereo and silhouette fusion for image-based modeling

This paper presents a volumetric stereo and silhouette fusion algorithm for acquiring high quality models from multiple calibrated photographs. Our method is based on computing and merging depth maps. Different from previous methods of this category, the silhouette information is also applied in our algorithm to recover the shape information on the textureless and occluded areas. The proposed algorithm starts by computing visual hull using a volumetric method in which a novel projection test method is proposed for visual hull octree construction. Then, the depth map of each image is estimated by an expansion-based approach that returns a 3D point cloud with outliers and redundant information. After generating an oriented point cloud from stereo by rejecting outlier, reducing scale, and estimating surface normal for the depth maps, another oriented point cloud from silhouette is added by carving the visual hull octree structure using the point cloud from stereo to restore the textureless and occluded surfaces. Finally, Poisson Surface Reconstruction approach is applied to convert the oriented point cloud both from stereo and silhouette into a complete and accurate triangulated mesh model. The proposed approach has been implemented and the performance of the approach is demonstrated on several real data sets, along with qualitative comparisons with the state-of-the-art image-based modeling techniques according to the Middlebury benchmark.     

GPU-based parallel construction of compact visual hull meshes

Building a visual hull model from multiple two-dimensional images provides an effective way of understanding the three-dimensional geometries inherent in the images. In this paper, we present a GPU accelerated algorithm for volumetric visual hull reconstruction that aims to harness the full compute power of the many-core processor. From a set of binary silhouette images with respective camera parameters, our parallel algorithm directly outputs the triangular mesh of the resulting visual hull in the indexed face set format for a compact mesh representation. Unlike previous approaches, the presented method extracts a smooth silhouette contour on the fly from each binary image, which markedly reduces the bumpy artifacts on the visual hull surface due to a simple binary in/out classification. In addition, it applies several optimization techniques that allow an efficient CUDA implementation. We also demonstrate that the compact mesh construction scheme can easily be modified for also producing a time- and space-efficient GPU implementation of the marching cubes algorithm.     

增量式精确多面体可见外壳

提出一种新的增量式计算精确多面体可见外壳的算法IEPVH。首先,在新视图的图像平面,计算旧可见外壳的边被新光椎切割得到的交点。然后,恢复旧可见外壳的边上交点的局部方向信息并同时获得新光椎边上的交点。接着,恢复新光椎边上交点的局部方向信息。最后,新可见外壳的多边形面片通过一次遍历网格的边的过程被识别出来,并为了便于显示而被划分为三角面片。与EPVH等其他算法相比,IEPVH不但能够让用户更多地参与基于图像3维重建的过程,而且具有空间计算复杂度小。实验证明此算法的高效和鲁棒性。IEPVH的特点使其更易于在移动设备中得到应用。     

采用窄带图切割的多目重构方法

针对目前多目重构研究中物体表面获取和凹陷区域恢复的难题,提出采用窄带图切割的可见外壳和多目立体匹配相融合的方法.在可见外壳的拓扑约束下,通过最小化能量泛函使得重构表面与多目立体匹配得到的稠密点云形状一致;并利用窄带图切割计算能量泛函的全局最优解.实验结果表明,该方法能重构封闭表面和凹陷区域;窄带图切割在获得最优解的同时提高了计算效率,与全图切割相比速度提高了3倍以上.     

Efficient Polyhedral Modeling from Silhouettes

Modeling from silhouettes is a popular and useful topic in computer vision. Many methods exist to compute the surface of the visual hull from silhouettes, but few address the problem of ensuring good topological properties of the surface, such as manifoldness. This article provides an efficient algorithm to compute such a surface in the form of a polyhedral mesh. It relies on a small number of geometric operations to compute a visual hull polyhedron in a single pass. Such simplicity enables the algorithm to combine the advantages of being fast, producing pixel-exact surfaces, and repeatably yield manifold and watertight polyhedra in general experimental conditions with real data, as verified with all datasets tested. The algorithm is fully described, its complexity analyzed and modeling results given.     

一种可视外壳的快速拓扑生成算法

针对各种传统可视外壳生成算法中数据冗余及壮健性不足等问题，提出了一种新的从阴影图像中快速重构物体可视外壳的壮健的算法，即首先利用物体表面的拓扑结构直接生成外壳，然后使用改进的SurfaceNet算法光滑三维表面，从而在保留经典的体求交方法壮健性的基础上，克服了对于物体内部点的冗余计算和存储问题，不仅使得计算的时间复杂度降低到仅线性依赖于外壳上结点的数目，而且降低了像片数对算法复杂度产生的影响，实验结果表明，在算法复杂度和壮健性上优于诸如八叉树等传统可视外壳算法。     

3D finite element mesh generation of complicated tooth model based on CT slices

An interactive three-dimensional finite element generation method is presented for modelling a multi-connected teeth and mandible structure. The tetrahedron is chosen as the basic element type due to its rigorous adaptability to structures with geometric complexities. The mesh generation is implemented by allocating two quadrangles in adjacent CT image slices to form a set of tetrahedrons. By examining all the possible allocations and their degradations, an algorithm is developed for interactive mesh generation, resulting in a series of tetrahedrons consistent with all the others without overlapping and spacing. The developed system was applied to a tooth-mandibular structure, generating a complicated 3D FEM model consisting of 4762 nodes and 18,534 tetrahedral elements with nine different materials. This 3D model was successfully used to evaluate different tooth restoration strategies, which proved the viability and effectiveness of the proposed method.     

逼近散乱点云数据的三角形网格精确剖分

基于自组织特征映射神经网络构建的三角形网格模型可以实现测量点云 压缩后的Delaunay 三角逼近剖分,但该模型存在逼近误差和边缘误差。为减小三角形网格 的逼近误差和边缘误差,构建了精确逼近的三角形网格模型。首先采用整个测量点云,对三 角形网格模型中的所有神经元进行整体训练;然后对三角形网格中的网格神经元的位置权 重,沿网格顶点法矢方向进行修正;最后采用测量点云中的边界点集,对三角形网格模型中 的网格边界神经元进行训练。算例表明,应用该模型,可以有效减小三角形网格的边缘误差, 三角形网格逼近散乱点云的逼近精度得到大幅提高并覆盖散乱点云整体分布范围。     

一种三维离散点数据生成非结构四面体算法

3D离散点数据的Delaunay三角剖分是构造曲面网格的关键技术之一。针对常用的基于三角网递推原理的Delaunay四面体局部构造生成算法中往往存在的四面体不相容问题,本文提出在当前点的局部计算中构造新四面体时,除了参考当前局部计算之前已生成的四面体集约束关系外,同时考虑当前点局部计算过程中生成的四面体集约束关系的非结构四面体生成算法,从而改善了新生成四面体与已有四面体的不相容性。文中最后给出的实验结果验证了本文算法的有效性。     

采用变形模型的图像序列可视外壳计算方法

针对基于图像序列变形模型三维重建中至关重要的初始模型计算问题,提出一种基于图像序列计算可视外壳的方法.该方法基于Snake活动轮廓变形模型,将真实物体可视外壳的计算问题转化为初始曲面通过内外力作用驱动收敛于目标物体的问题.以真实物体图像序列中提取的物体轮廓为输入,根据轮廓信息和光滑度信息计算内力及轮廓力大小,驱动球体初始曲面变形收敛于可视外壳;在变形过程中加入删除短边、分裂长边、对角线翻转等网格优化操作,以避免发生网格错乱.实验结果表明,文中方法有效地克服了传统的基于体元素细分产生网格算法网格质量不高的缺点,且参数可调,易于实现,占用内存少,生成的曲面更加光滑,细节恢复效果理想.     

From laser-scanned data to feature human model: a system based on fuzzy logic concept

This paper describes the development of a prototype system using fuzzy logic concept for constructing a feature human model, which is to be stored in a 3D digital human model database. In our approach, the feature human model is constructed by unorganized cloud points obtained from 3D laser scanners. Firstly, noisy points are removed, and the orientation of the human model is adjusted; secondly, a feature based mesh generation algorithm is applied on the cloud points to construct the mesh surface of a human model; lastly, semantic features of the human model are extracted from the mesh surface. Compared with earlier approach, our method strongly preserves the topology of a human model; more details can be constructed; and both the robustness and the efficiency of the algorithm are improved. At the end of the paper, in order to demonstrate the functionality of feature human models, potential applications are given.     

平面点集凸包快速构建算法的研究

文章提出了一种提高构建凸包速度的新方法。该算法生成一个网格来管理离散点,在淘汰明显不位于凸包上的点时,将对离散点的取舍转换为对格的取舍,计算工作量只与离散点的范围及网格的密度有关,与离散点的数目无关;同时对点集也进行了初略的排序。在求取剩余点集的凸包时,采用了一种先分段求取凸包边界,最后将这些边界合并成凸包的方法,该方法充分利用了剩余点集所具有的有序性。     

基于二维轮廓序列的膝关节三维重建

提出了一个基于二维轮廓序列的四面体网格生成方法,用于医学图像三维几何模型重构.该方法首先对各选定的断层图像提取目标轮廓并做分支匹配等处理,然后生成各轮廓内部平面域的三角网格,最后在相邻断层之间根据三角网格连接四面体单元.该方法被应用于人体膝关节虚拟手术系统的三维几何建模,得到的膝部股骨模型包含494个节点和2 046个四面体单元,膝部脂肪模型包含2 854个节点和14011个四面体单元,这些模型被成功地应用于膝关节手术仿真,从而证明了该三维模型重建方法的可行性和有效性.     

A mesh reconstruction algorithm driven by an intrinsic property of a point cloud

This paper presents an algorithm for reconstructing a triangle mesh surface from a given point cloud. Starting with a seed triangle, the algorithm grows a partially reconstructed triangle mesh by selecting a new point based on an intrinsic property of the point cloud, namely, the sampling uniformity degree. The reconstructed mesh is essentially an approximate minimum-weight triangulation to the point cloud constrained to be on a two-dimensional manifold. Thus, the reconstructed surface has only small topological difference from the surface of the sampled object. Topological correct reconstruction can be guaranteed by adding a post-processing step.     

Models and algorithms for efficient multiresolution topology estimation of measured 3-D range data

In this paper, we propose a new efficient topology estimation algorithm to construct a multiresolution polygonal mesh from measured three-dimensional (3-D) range data. The topology estimation problem is defined under the constraints of cognition, compactness, and regularity, and the algorithm is designed to be applied to either a cloud of points or a dense mesh. The proposed algorithm initially segments the range data into a finite number of Voronoi patches using the K-means clustering algorithm. Each patch is then approximated by an appropriate polygonal and eventually a triangular mesh model. In order to improve the equiangularity of the mesh, we employ a dynamic mesh model, in which the mesh finds its equilibrium state adaptively, according to the equiangularity constraint. Experimental results demonstrate that satisfactory equiangular triangular mesh models can be constructed rapidly at various resolutions, while yielding tolerable modeling error.     

一种基于改进PointNet++网络的三维手姿估计方法

针对PointNet++网络处理点云局部特征时因分组范围区过大导致计算量较大的问题,提出一种改进的PointNet++网络的三维手姿估计方法。首先对手势点云进行基于Delaunay三角剖分算法与K中位数聚类算法相结合的三角剖分,得到手势点云的三角网格模型,并计算三角网格模型的边长均值;然后以三角网格模型边长均值为半径,对最远点采样(FPS)的采样点进行球查询搜索,再根据搜索到的采样点个数极值对采样点云进行K近邻分组,并最终输入PointNet网络,完成三维手姿的位置估计。改进后的PointNet++网络可以根据不同的点云密度自动调整网络分组区域的局部提取点个数。实验结果表明,在不影响三维手姿估计精度的情况下,该方法提高了PointNet++网络的模型训练速度,并在三维手姿估计中可有效减少特征提取的计算量,使计算机能够更快地捕捉手姿状态。     

体素可视外壳并行优化建模方法

基于体的可视外壳重建不需要复杂的几何计算,易于实现,但其精度和效率仍不理想。提出一种基于统一计算架构(CUDA)和行进立方体(MC)的体素可视外壳并行优化建模方法,将体素相交判断和等值面抽取过程并行分解,利用CUDA的内核多线程机制加速建模;在MC网格化阶段,提取准确交点并消除冗余边计算;利用像素着色器实现混合权重的纹理映射,提高模型精度。在多摄像机采集装置DreamWorld上的实验结果表明,本文方法能够对多个动态物体进行实时准确的3维建模。     

基于视点的网格模型快速简化算法

在计算机图形学中，经常采用网格模型对物体和场景进行描述，而网格模型的大数据量成为实时绘制的瓶颈。因此，必须对网格模型进行简化，目前的简化算法，主要是以网格模型几何误差的最小化为准则，而忽略了模型的视觉特征。文章提出了一种基于视点的网格模型简化算法，其简化准则是视觉特征的最优化，利用视点相关，建立视点与网格精度的对应关系，对距离视点较近的部分采用较密网格，对距视点较远的部分采用稀疏网格。实验结果表明，该算法能够在保证高度真实感视觉效果的前提下，实现模型较大幅度的简化。     

An O((log log n)2) time algorithm to compute the convexhull of sorted points on reconfigurable meshes

The problem of computing the convex hull of a set of n sorted points in the plane is one of the fundamental tasks in image processing, pattern recognition, cellular network design, and robotics, among many others. Somewhat surprisingly, in spite of a great deal of effort, the best previously known algorithm to solve this problem on a reconfigurable mesh of size √n×√n was running in O(log2 n) time. It was open for more than ten years to obtain an algorithm for this important problem running in sublogarithmic time. Our main contribution is to provide the first breakthrough: we propose an almost optimal convex hull algorithm running in O((log log n)²) time on a reconfigurable mesh of size √n×√n. With slight modifications, this algorithm can be implemented to run in O((log log n)²) time on a reconfigurable mesh of size √n/loglogn×√n/loglogn. Clearly, the latter algorithm is work-optimal. We also show that any algorithm that computes the convex hull of a set of n sorted points on an n-processor reconfigurable mesh must take Ω(log log n) time. Our result opens the door to an entire slew of efficient convex-hull-based algorithms on reconfigurable meshes     

一种基于凸包近似的快速体积计算方法

体积是物体的基本几何属性,在许多应用场合需要频繁地被计算。目前基本上通过重构物体曲面而间接求取体积,增加了许多不必要的工作。提出一种快速求取点云模型体积的方法,使用增量式算法计算点云的凸包用来近似物体,将凸包分解成上下两个三角网格面,使用正投影法分别求取它们的投影体积,它们两者之差即是所求模型体积。实验表明该算法实现简单,可快速地求解处理具有任何几何和拓扑复杂性的点云模型。