伪点云修正增强激光雷达数据

目的 激光雷达在自动驾驶中具有重要意义,但其价格昂贵,且产生的激光线束数量仍然较少,造成采集的点云密度较稀疏。为了更好地感知周围环境,本文提出一种激光雷达数据增强算法,由双目图像生成伪点云并对伪点云进行坐标修正,进而实现激光雷达点云的稠密化处理,提高3D目标检测精度。此算法不针对特定的3D目标检测网络结构,是一种通用的点云稠密化方法。方法 首先利用双目RGB图像生成深度图像,根据先验的相机参数和深度信息计算出每个像素点在雷达坐标系下的粗略3维坐标,即伪点云。为了更好地分割地面,本文提出了循环RANSAC （random sample consensus）算法,引入了一个分离平面型非地面点云的暂存器,改进复杂场景下的地面分割效果。然后将原始点云进行地面分割后插入KDTree （k-dimensional tree）,以伪点云中的每个点为中心在KDTree中搜索若干近邻点,基于这些近邻点进行曲面重建。根据曲面重建结果,设计一种计算几何方法导出伪点云修正后的精确坐标。最后,将修正后的伪点云与原始激光雷达点云融合得到稠密化点云。结果 实验结果表明,稠密化的点云在视觉上具有较好的质量,物体具有更加完整的形状和轮廓,并且在KITTI （Karlsruhe Institute of Technology and Toyota Technological Institute）数据集上提升了3D目标检测精度。在使用该数据增强方法后,KITTI数据集下AVOD （aggregate view object detection）检测方法的AP_3D-Easy （average precision of 3D object detection on easy setting）提升了8.25%,AVOD-FPN （aggregate view object detection with feature pyramid network）检测方法的AP_BEV-Hard （average precision of bird’s eye view on hard setting）提升了7.14%。结论 本文提出的激光雷达数据增强算法,实现了点云的稠密化处理,并使3D目标检测结果更加精确。     

A fast registration algorithm of rock point cloud based on spherical projection and feature extraction

Point cloud registration is an essential step in the process of 3D reconstruction. In this paper, a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP) algorithm. In our proposed algorithm, the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates, then image features are extracted and edge points are removed, the features used in this algorithm is scale-invariant feature transform (SIFT). By analyzing the corresponding relationship between digital images and 3D points, the 3D feature points are extracted, from which we can search for the two-way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling, the transformation is computed via the Levenberg-Marquardt-Iterative Closest Point (LM-ICP) algorithm. Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.     

基于双目视觉的基准差梯度立体匹配法􀀂

因灰度相关只是从一个侧面来描述左右图像特征点区域之间的灰度相似性,没有考虑特征点之间的空间相关性,因此利用灰度间的相似性作为测量标准进行匹配,不可避免地出现误匹配,提出了在进行双目视觉立体匹配时,采用灰度相关匹配技术,提取复峰特征点作为初始匹配集,采用视差梯度有限约束优化初始匹配集.利用左右图像一对已知对应基准点,通过计算基准点与复峰集各点间的基准差梯度,采用基准差梯度极小化评判标准,确定唯一匹配,并将匹配结果确定为新的基准点以不断更新基准点,直至左(右)图像特征点匹配完毕.通过分别对一幅弱纹理实际自然图像及已知三维坐标标准件的三维重建,证实了所提方法的有效性和可靠性.     

A computational model of bounded developable surfaces with application to image‐based three‐dimensional reconstruction

Developable surfaces have been extensively studied in computer graphics because they are involved in a large body of applications. This type of surfaces has also been used in computer vision and document processing in the context of three‐dimensional (3D) reconstruction for book digitization and augmented reality. Indeed, the shape of a smoothly deformed piece of paper can be very well modeled by a developable surface. Most of the existing developable surface parameterizations do not handle boundaries or are driven by overly large parameter sets. These two characteristics become issues in the context of developable surface reconstruction from real observations. Our main contribution is a generative model of bounded developable surfaces that solves these two issues. Our model is governed by intuitive parameters whose number depends on the actual deformation and including the “flat shape boundary”. A vast majority of the existing image‐based paper 3D reconstruction methods either require a tightly controlled environment or restricts the set of possible deformations. We propose an algorithm for reconstructing our model's parameters from a general smooth 3D surface interpolating a sparse cloud of 3D points. The latter is assumed to be reconstructed from images of a static piece of paper or any other developable surface. Our 3D reconstruction method is well adapted to the use of keypoint matches over multiple images. In this context, the initial 3D point cloud is reconstructed by structure‐from‐motion for which mature and reliable algorithms now exist and the thin‐plate spline is used as a general smooth surface model. After initialization, our model's parameters are refined with model‐based bundle adjustment. We experimentally validated our model and 3D reconstruction algorithm for shape capture and augmented reality on seven real datasets. The first six datasets consist of multiple images or videos and a sparse set of 3D points obtained by structure‐from‐motion. The last dataset is a dense 3D point cloud acquired by structured light. Our implementation has been made publicly available on the authors' web home pages. Copyright © 2012 John Wiley & Sons, Ltd.     

面向RGBD深度数据的快速点云配准方法

目的 真实物体的3维重建一直是计算机图形学、机器视觉等领域的研究热点。针对基于RGBD数据的非匀速非固定角度旋转物体的3维重建问题,提出一种利用旋转平台重建物体3维模型的配准方法。方法 首先通过Kinect采集位于旋转平台上目标物的深度数据和颜色数据,对齐融合并使用包围盒算法去除背景噪声和不需要的外部点云,获得带有颜色信息的点云数据。并使用基于标定物不同角度上的点云数据标定出旋转平台中心轴的位置,从而获得Kinect与旋转平台之间的相对关系;然后通过曲率特征对目标点云进行特征点提取并寻找与相邻点云的对应点;其中对于特征点的选取,首先针对点云中的任意一点利用kd-tree搜寻其k个邻近点,对这些点进行曲面拟合,进而计算其高斯曲率,将高斯曲率绝对值较大的n个点作为点云的特征点。n的取值由点云的点个数、点密度和复杂度决定,具体表现为能反映物体的大致轮廓或表面特征信息即可。对于对应点的选取,考虑到欧氏距离并不能较好反映点云中的点对在旋转过程中的对应关系,在实际配准中,往往会因为点云重叠或距离过远等原因找到大量错误的对应点。由于目标物在扫描过程中仅绕旋转轴进行旋转,因此采用圆弧最小距离寻找对应点可有效减少错误点对。随后,使用二分迭代寻找绕中心轴的最优旋转角度以满足点云间的匹配误差最小;最后,将任意角度获取的点云数据配准到统一的坐标系下并重建模型。结果 使用斯坦福大学点云数据库和自采集数据库分别对该方法和已有方法在算法效率和配准结果上进行对比实验,实验结果显示在拥有平均75 000个采样点的斯坦福大学点云数据库上与传统ICP算法和改进ICP算法相比,迭代次数分别平均减少86.5%、57.5%,算法运行时间分别平均减少87%、60.75%,欧氏距离误差平方和分别平均减少70%、22%;在具有平均57000个采样点的自采集点云数据库上与传统ICP算法和改进ICP算法相比,迭代次数分别平均减少94%、75%,算法运行时间分别平均减少92%、69%,欧氏距离误差平方和分别平均减少61.5%、30.6%;实验结果显示使用该方法进行点云配准效率较高且配准误差更小;和KinectFusion算法相比在纹理细节保留上也表现出较好的效果。结论 本文提出的基于旋转平台标定的点云配准算法,利用二分迭代算法能够有效降低算法复杂度。与典型ICP和改进的ICP算法的对比实验也表明了本文算法的有效性。另外,与其他方法在具有纹理的点云配准对比实验中也验证了本文配准方法的优越性。该方法仅采用单个Kinect即可实现对非匀速非固定角度旋转物体的3维建模,方便实用,适用于简单快速的3维重建应用场合。     

点云数据集的隐式曲面重构研究进展

点云数据的曲面重建就是对扫描设备获得的物体散乱数据点重建三维物体表面,它被广泛应用于计算机动画、目标识别、数据可视化以及地理信息系统。点云的隐式曲面重建由于能够去除点云噪声,修补孔洞和裂缝,不需要拼接和平滑等后续处理,成为点云数据集曲面重构的重要方法。文中综述了目前一些主要的隐式曲面重构方法,就隐式模型以及相应的曲面重构算法的优缺点进行了分析比较,并对隐式曲面重构存在的问题和未来发展方向作了相应的分析和讨论。     

Three-dimensional reconstruction for scanning electron microscope

The paper describes the reconstruction algorithm of the 3D shape of a microobject’s surface based on an image stereopair, acquired with a Scanning Electron Microscope (SEM). Presented is the model of constructing the image in a SEM. The corresponding points are detected by using the SIFT algorithm. Employing the RANSAC method the points of correspondence are used to define the relative position of an object in a stereopair. A point cloud is created in a 3D space and interpolated in order to reconstruct a 3D surface.     

基于曲面局平特性的散乱数据拓扑重建算法

提出了一种基于曲面局平特性的,以散乱点集及其密度指标作为输入,以三角形分片线性曲面作为输出的拓扑重建算法.算法利用曲面的局平特性,从散乱点集三维Delaunay三角剖分的邻域结构中完成每个样点周围的局部拓扑重建,并从局部重建的并集中删除不相容的三角形,最终得到一个二维流形拓扑曲面集作为重建结果.该算法适应于包括单侧曲面在内的任意不自交的拓扑曲面集,并且重建结果是相对优化的曲面三角形剖分,可以应用于科学计算可视化、雕塑曲面造型和反求工程等领域.     

Estimation of planes of a rock mass in a gallery wall from point cloud data based on MD PSO

LiDAR (laser imaging detection and ranging) has been developed to measure the distance of the mesh of points on an object with a high level of accuracy. It provides high-resolution point cloud data as a result of distance measurement. Detailed 3D shapes of objects can be estimated from point cloud data. LiDAR has been used to identify discontinuities in a rock mass of a tunnel gallery wall. To identify discontinuities, it is necessary to approximate the rock mass surface with small planes. Normal vectors of the planes are important to identify discontinuities. We developed an algorithm for estimation of planes based on multi-dimensional particle swarm optimization (MD PSO) from point cloud data. Point cloud data were segmented into bounding boxes and grouped into clusters by MD PSO. Planes were estimated using the least squares method for point cloud data in the respective clusters. The newly developed algorithm based on MD PSO was evaluated using point cloud data obtained from a gallery wall. Evaluation was carried out in comparison with the previous developed variable-box segmentation (VBS) algorithm. The MD PSO-based algorithm showed a 7% higher accuracy than that of the VBS algorithm.     

基于特征提取的点云自动配准优化研究

针对三维点云自动配准精度不高、鲁棒性不强等问题,提出一种基于判断点云邻域法向量夹角的自动配准算法。该算法首先计算点云中每个点的法向量与邻域点集的法向量夹角的余弦值,然后把邻域各点的余弦值作为该点的属性特征向量,进行特征分类提取特征点,根据几何特征的相似性初步搜索匹配点对,并采用欧式距离约束条件剔除匹配错误的点对;运用最小二乘法计算初始配准参数,再通过改进的迭代最近点（Iterative Closest Point,ICP）算法进行精匹配。实验证明,该算法相对于经典的ICP算法无论收敛速度还是匹配精度上都有提升。     

一种无标记点三维点云自动拼接技术

基于相位投影和双目的三维光学测量系统已经广泛应用于各领域.受投影光测量系统单次测量范围大小的限制,对大型物体的测量需要在表面粘贴圆形标记点进行多次拼接的缺点,探讨一种基于SIFT的无标记点自动拼接技术.该技术采用SIFT方法获取两次测量的特征点,其次结合RANSAC求出图像特征点的匹配关系,再根据立体匹配中图像特征点与三维点云之间的对应关系,将二维特征点的对应关系映射到三维点云的对应关系上,最后由SVD奇异值分解算法求得旋转和平移矩阵实现拼接.实验证明:该方法可以避免在被测量对象上粘贴标记点,能够快速准确地实现自动拼接.     

结构光视觉三维点云逐层三角网格化算法

针对结构光视觉恢复的大规模三维点云的可投影特点,提出一种基于投影网格的底边驱动逐层网格化曲面重建算法。该算法首先将点云投影到一个二维平面上;然后基于点云投影区域建立规则投影网格,并将投影点映射到规则二维投影网格上,建立二维网格点与三维点云间的映射关系;接着对投影网格进行底边驱动的逐层网格化,建立二维三角网格;最后根据二维投影点与三维点的对应关系及二维三角网格拓扑关系获得最终的三维网格曲面。实验结果表明,算法曲面重建速度快,可较好地保持曲面细节特征。     

Stereo Image Analysis of Non-Lambertian Surfaces

Stereo image analysis is based on establishing correspondences between a pair of images by determining similarity measures for potentially corresponding image parts. Such similarity criteria are only strictly valid for surfaces with Lambertian (diffuse) reflectance characteristics. Specular reflections are viewpoint dependent and may thus cause large intensity differences at corresponding image points. In the presence of specular reflections, traditional stereo approaches are often unable to establish correspondences at all, or the inferred disparity values tend to be inaccurate, or the established correspondences do not belong to the same physical surface point. The stereo image analysis framework for non-Lambertian surfaces presented in this contribution combines geometric cues with photometric and polarimetric information into an iterative scheme that allows to establish stereo correspondences in accordance with the specular reflectance behaviour and at the same time to determine the surface gradient field based on the known photometric and polarimetric reflectance properties. The described approach yields a dense 3D reconstruction of the surface which is consistent with all observed geometric and photopolarimetric data. Initially, a sparse 3D point cloud of the surface is computed by traditional blockmatching stereo. Subsequently, a dense 3D profile of the surface is determined in the coordinate system of camera 1 based on the shape from photopolarimetric reflectance and depth technique. A synthetic image of the surface is rendered in the coordinate system of camera 2 using the illumination direction and reflectance properties of the surface material. Point correspondences between the rendered image and the observed image of camera 2 are established with the blockmatching technique. This procedure yields an increased number of 3D points of higher accuracy, compared to the initial 3D point cloud. The improved 3D point cloud is used to compute a refined dense 3D surface profile. These steps are iterated until convergence of the 3D reconstruction. An experimental evaluation of our method is provided for areas of several square centimetres of forged and cast iron objects with rough surfaces displaying both diffuse and significant specular reflectance components, where traditional stereo image analysis largely fails. A comparison to independently measured ground truth data reveals that the root-mean-square error of the 3D reconstruction results is typically of the order 30–100 μm at a lateral pixel resolution of 86 μm. For two example surfaces, the number of stereo correspondences established by the specular stereo algorithm is several orders of magnitude higher than the initial number of 3D points. For one example surface, the number of stereo correspondences decreases by a factor of about two, but the 3D point cloud obtained with the specular stereo method is less noisy, contains a negligible number of outliers, and shows significantly more surface detail than the initial 3D point cloud. For poorly known reflectance parameters we observe a graceful degradation of the accuracy of 3D reconstruction.     

基于Sparse ICP 的三维点云耳廓识别

提出一种新颖的三维耳廓识别方法,首先基于PCA 和SVD 分解对三维耳廓点云模 型进行归一化预处理,以统一数据库中所有耳廓点云模型的位置与姿态;然后基于Iannarelli 分 类系统提取三维耳廓的4 个局部特征区域,并利用Sparse ICP 算法对局部特征区域进行匹配;最 后根据局部特征区域中对应点间的距离判断耳廓之间的差异测度,实现耳廓形状识别。实验证明, 本文算法与其他算法相比具有较高的识别精度和识别效率。     

改进PMVS三维重建点云滤波算法

针对在小范围场景进行单目视觉三维重建过程中,稠密点云模型存在大量离群点的现象,提出一种改进的点云滤波算法。将多视图稠密重建(Patch-based Multi-View Stereo,PMVS)算法与统计分析法相融合,对利用PMVS算法得到的稠密点云进行统计分析,设定标准距离并求解点云中每一个点到其所有邻近点的平均距离,去除平均距离大于标准距离的点。实验结果表明,融合后的点云滤波算法不仅剔除了大量离群点,还在保证目标物体细节特征的情况下对冗余的特征点进行一定程度上的消除,在提高重建表面真实度和精度的同时,为后期测量装配工作提供了可靠模型。     

Robust dense reconstruction by range merging based on confidence estimation

Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.     

一种城区LiDAR点云数据的抽稀算法

当城区LiDAR点云数据密度较大时，存在大量的数据冗余，造成了计算量大、效率低、显示不便等一系列问题，使得建筑物的三维可视化及三维重建等应用受到较大挑战。针对该问题，结合泊松碟采样在测地空间中的地形自适应特点，提出了适用于城区LiDAR点云数据的抽稀算法。泊松碟采样随机将与已有采样点的测地距离大于某一阈值的点加入采样点集，并不断重复这一过程直至没有新的采样点加入为止。在此基础上，依据LiDAR点云数据的特点，定义了一种新的与所选点与其邻域内其他点间高度差标准差相关的加权测地距离，改进了泊松碟采样算法。该方法能有效调整城区建筑物的采样率，从而尽可能地保持建筑物的原始特征，并保留良好的可视化效果。四组对比实验结果表明了该算法的适用性及高效性。     

一种城区LiDAR点云数据的抽稀算法

当城区LiDAR点云数据密度较大时,存在大量的数据冗余,造成了计算量大、效率低、显示不便等一系列问题,使得建筑物的三维可视化及三维重建等应用受到较大挑战。针对该问题,结合泊松碟采样在测地空间中的地形自适应特点,提出了适用于城区LiDAR点云数据的抽稀算法。泊松碟采样随机将与已有采样点的测地距离大于某一阈值的点加入采样点集,并不断重复这一过程直至没有新的采样点加入为止。在此基础上,依据LiDAR点云数据的特点,定义了一种新的与所选点与其邻域内其他点间高度差标准差相关的加权测地距离,改进了泊松碟采样算法。该方法能有效调整城区建筑物的采样率,从而尽可能地保持建筑物的原始特征,并保留良好的可视化效果。四组对比实验结果表明了该算法的适用性及高效性。