A CNN-based 3D patch registration approach for integrating sequential models in support of progress monitoring

Significant advancements in three-dimensional (3D) imaging technologies have enabled the ability to effectively monitor and manage the progress of works in construction. Traditionally, 3D point clouds have been used in conjunction with building information models to visualize the progress of works. The discrepancies between ‘as-planned’ and ‘actual’ models are unable to be automatically identified using the existing approaches due the absence of an effective registration algorithm. To ensure the registration accuracy of multi-scanned point clouds, an automated method based on a data-driven Convolutional Neural Network (CNN) deep learning algorithm is proposed. In this instance, 3D Point cloud patches are aligned with spatial datasets that are scanned from different locations using range cameras. The registration results are used to automatically detect spatial changes when compared with different point clouds. The quantified changes are utilized to determine the percentage of work that has been completed at fixed intervals. The developed registration approach is tested and validated using a series of experiments. It is demonstrated that discrepancies between ‘as-planned’ and ‘actual’ models can be identified with a higher level of accuracy, which can enable the baseline for monitoring construction to be undertaken in real-time.     

Assessing the impact of 3D point neighborhood size selection on unsupervised spall classification with 3D bridge point clouds

The performance of point cloud-based defect classification algorithms depends on the quality of the computed geometric features, which in turn is strongly affected by the selection of the neighborhood size of local 3D points. Many existing algorithms select a single scene-specific value as the neighborhood size parameter and apply it uniformly to all 3D points while computing features. By doing so, some defect features end up being smoothened out at locations where the selected neighborhood size is larger than the optimal choice for those points. Conversely, geometric features might not always capture the local 3D structure if they are calculated based on less than optimal number of neighbors. This paper investigates and assesses the relationship between neighborhood size selection and the performance of point cloud-based unsupervised spall classification algorithms in a quantitative manner. Among the presented neighborhood selection approaches, an entropy-based approach that incorporates a tailored optimal neighborhood size for every point in a point cloud, resulted in a significant improvement over the performance of current state-of-the-art approaches. The performed quantitative study also demonstrated the robustness of this approach to variables, such as subsampling percentage, maximum neighborhood size and different noise levels. The implemented research testbed is comprised of statistically significant number of spall defect datasets generated from five different bridges. The outcome of this research is expected to improve the reliability of point cloud-based condition assessment for concrete bridges.     

基于分层策略的三维非刚性模型配准算法

在三维非刚性模型分析中,通常需要对不同姿态下的模型进行配准。针对传统配准算法存在复杂度高、计算量大、精确度低等问题,提出一种新的基于分层策略的三维非刚性模型配准算法。首先,定义热核签名函数为模型的标量域,使用同源聚类算法提取模型的特征点和特征区域,进而提出三维几何模型的树形表示方法:它的根节点为三维几何模型,内部节点为模型的特征区域,叶节点为包含在相应区域的特征点。然后,根据三维几何模型的树形表示提出模型的分层配准算法。在SHREC 2010模型配准数据集上对比分析了分层配准算法、推广的多维尺度变换算法（GMDS）和博弈论方法在等距变换、孔洞、小孔洞、尺度变换、局部尺度变换、重采样、噪声、散粒噪声以及拓扑变换等情况下的性能。实验结果表明,在以上三维几何模型数据受干扰的情况下,分层配准算法的准确性明显优于GMDS方法和博弈论方法,同时具有较低的计算复杂度。     

Taiwanese adult foot shape classification using 3D scanning data

This study classifies the foot shapes of Taiwanese using 3D foot scanning data from 2000 males and 1000 females. Nine foot dimensions relative to foot length and absolute measures in the common foot length categories were applied to compare the gender differences. Using foot breadth in % foot length (% FL), ball of foot length in % FL and arch height in % FL as feature parameters, three foot shape types for males and females can be classified. Significant gender differences were found in seven of the nine foot dimensions. Females had greater ball of foot length than males (0.2% FL). When comparing feet of the same foot length, males had greater breadth, girth and height dimensions than females, except for toe height. In addition, ethnic differences in foot shape were also observed. The findings can provide very useful information for building gender-specific shoe lasts and designing footwear insoles.     

三维激光扫描表面数据区域分割

针对现有三维激光扫描数据区域分割算法受原始碎片表面粗糙度影响较大且只适用于形状较规则、表面较平坦及断裂面较少的物体这一问题,提出了区域膨胀策略的三维扫描表面数据区域分割算法,该算法将三维激光扫描表面数据分割成若干个具有相同法矢方向的区域。首先将三维扫描表面数据转化为三维网格模型;然后利用同一区域中相邻网格具有相似法线方向这一性质,使用区域膨胀策略生成若干获选表面区域;最后通过去除候选区域中的噪声区域得到最终表面区域分割结果。通过实物表面扫描数据对上述算法进行仿真验证,结果表明该算法可对三维表面扫描数据进行有效的区域分割。     

Multi-contour initial pose estimation for 3D registration

Reliable manipulation of everyday household objects is essential to the success of service robots. In order to accurately manipulate these objects, robots need to know objects’ full 6-DOF pose, which is challenging due to sensor noise, clutters, and occlusions. In this paper, we present a new approach for effectively guessing the object pose given an observation of just a small patch of the object, by leveraging the fact that many household objects can only keep stable on a planar surface under a small set of poses. In particular, for each stable pose of an object, we slice the object with horizontal planes and extract multiple cross-section 2D contours. The pose estimation is then reduced to find a stable pose whose contour matches best with that of the sensor data, and this can be solved efficiently by cross-correlation. Experiments on the manipulation tasks in the DARPA Robotics Challenge validate our approach. In addition, we also investigate our method’s performance on object recognition tasks raising in the challenge.     

一种获取物体3维信息的多轴机器人激光扫描系统

针对机器人工作范围有限引起扫描范围小的问题,建立了一种由机器人?便携式线激光3维扫描仪以及旋转平台构成的多轴激光扫描系统。利用半径已知的球体作为参照工具,机器人手持便携式扫描仪对球体进行扫描,精确标定了便携式扫描仪与机器人的方位关系。同时,提出了一种标定转台中心轴线的方法,该方法利用旋转平台上的参考球,在两个不同高度的旋转圆心精确标定出了转轴的方位。实验结果表明,该多轴激光扫描系统可以多角度、多方位对大尺寸物体进行扫描,并有着稳定、灵活以及精度高的特点。     

基于单目视觉几何构建的三维扫描系统

为构建基于单目视觉的快速便捷式三维扫描系统,提出一种高精度的单目视觉几何投影的场景建模方法,并开发一种低成本高精度的三维扫描系统。首先,获取平面标定点的图像坐标,经投影变换将其转换为摄像坐标系下的三维空间位置坐标,分别建立平移台面和底座的三维空间平面方程;其次,通过移动平移台面求取同名标定点的空间坐标,求解平移台面的平移向量,并通过落在平移台面和底座上的激光线条求解激光平面;最后,提取图像中的激光光条中心点并将其变换为物体表面的三维点云数据。实验结果表明,投影变换求得的平面方程误差小于0．2％,扫描结果误差低于0．05mm。     

Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data

We propose a method for characterizing spatial region data. The method efficiently constructs a k-dimensional feature vector using concentric spheres in 3D (circles in 2D) radiating out of a region's center of mass. These signatures capture structural and internal volume properties. We evaluate our approach by performing experiments on classification and similarity searches, using artificial and real datasets. To generate artificial regions we introduce a region growth model. Similarity searches on artificial data demonstrate that our technique, although straightforward, compares favorably to mathematical morphology, while being two orders of magnitude faster. Experiments with real datasets show its effectiveness and general applicability.     

Evaluating 2D and 3D geovisualisations for basic spatial assessment

This study investigates the use of 2D and 3D presentations of maps for the assessment of distances in a geographical context. Different types of 3D representations have been studied: A weak 3D visualisation that provides static monocular depth cues and a strong 3D visualisation that uses stereoscopic and kinetic depth cues. Two controlled experiments were conducted to test hypotheses regarding subjects’ efficiency in visually identifying the shortest distance among a set of market locations in a map. As a general result, we found that participants were able to correctly identify shortest distances when the difference to potential alternatives was sufficiently large, but performance decreased systematically when this difference decreased. Noticeable differences emerged for the investigated visualisation conditions. Participants in this study were equally efficient when using a weak 3D representation and a 2D representation. When the strong 3D visualisation was employed, they reported visual discomfort and tasks solved were significantly less correct. Presentations of intrinsic 2D content (maps) in 3D context did not, in this study, benefit from cues provided by a strong 3D visualisation and are adequately implemented using a weak 3D visualisation.     

A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data

Segmentation of Magnetic Resonance Imaging (MRI) brain image data has a significant impact on the computer guided medical image diagnosis and analysis. However, due to limitation of image acquisition devices and other related factors, MRI images are severely affected by the noise and inhomogeneity artefacts which lead to blurry edges in the intersection of the intra-organ soft tissue regions, making the segmentation process more difficult and challenging. This paper presents a novel two-stage fuzzy multi-objective framework (2sFMoF) for segmenting 3D MRI brain image data. In the first stage, a 3D spatial fuzzy c-means (3DSpFCM) algorithm is introduced by incorporating the 3D spatial neighbourhood information of the volume data to define a new local membership function along with the global membership function for each voxel. In particular, the membership functions actually define the underlying relationship between the voxels of a close cubic neighbourhood and image data in 3D image space. The cluster prototypes thus obtained are fed into a 3D modified fuzzy c-means (3DMFCM) algorithm, which further incorporates local voxel information to generate the final prototypes. The proposed framework addresses the shortcomings of the traditional FCM algorithm, which is highly sensitive to noise and may stuck into a local minima. The method is validated on a synthetic image volume and several simulated and in-vivo 3D MRI brain image volumes and found to be effective even in noisy data. The empirical results show the supremacy of the proposed method over the other FCM based algorithms and other related methods devised in the recent past.     

A frontier-void-based approach for autonomous exploration in 3D

We consider the problem of an autonomous robot searching for objects in unknown 3D space. Similar to the well-known frontier-based exploration in 2D, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3D, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform using a 5-DOF manipulator searching for victims in a simulated urban search and rescue setup. The results indicate the real-world capability and search efficiency of the proposed method.     

Shadow based texture registration for 3D modeling of outdoor scenes

This paper presents a novel method for the registration of texture images with a 3D model of outdoor scenes. We pose image registration as an optimization problem that uses knowledge of the sun’s position to estimate shadows in a scene, and use the shadows produced as a cue to solve for the registration parameters. Results are presented on a controlled experiment and for a large scale model of an archaeological site in Sicily.     

基于三维激光扫描技术在地铁隧道形变监测的应用研究

针对传统的地铁隧道形变检测技术,无法全面反映隧道形变的问题,该文提出了基于三维激光扫描技术的地铁隧道形变监测,设计了三维振镜式激光扫描系统,包括控制系统、激光器、光学系统和振镜扫描系统,采用X振镜、Y振镜以及光学系统确保光束的质量,实现三维激光扫描技术。最后利用点云隧道三维建模算法,实现了地铁隧道的形变监测。实验表明,该文研究的系统测量精确度最高,与实际值仅差1 mm,监测10段地铁隧道所消耗的时间最少为20 s。     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

Heuristic solution for constrained 7-DOF motion planning in 3D scanning application

This paper proposes a heuristic algorithm for near-optimal handling of the redundancy in robot mechanisms, applied to a 3D scanning platform consisting of a 6-DOF articulated robot arm which moves a laser sensor around a workpiece placed on a rotary table. The proposed method handles both static constraints, like avoiding joint limits, kinematic singularities or collisions, modelled using a configuration map viewed as a grayscale image, and dynamic constraints like velocity and acceleration. The algorithm is compared with Dijkstra algorithm, which gives the optimal solution, but is very slow, and with two other heuristics which are very fast, but give suboptimal solutions.     

基于边缘对应的三维颅骨自动非刚性配准方法

针对三维颅骨模型在初始姿态相差较大以及存在较多缺失情况下自动配准困难的问题,提出一种基于边缘对应的三维颅骨非刚性自动配准方法。首先对待配准三维颅骨进行边缘提取,获得所有孔洞的边缘;然后根据边缘长度以及边缘间最短距离自动识别边缘类型,建立待配准颅骨和参考颅骨在边缘上的对应;之后对待配准颅骨的初始位置和姿态进行调整,实现粗配准;最后通过两次一致点漂移（CPD）算法逐步实现两个颅骨从边缘区域至所有区域的精确配准。实验结果表明,与常用的基于迭代最近点（ICP）和薄板样条函数（TPS）相结合的三维颅骨自动配准方法相比,该方法对姿态、位置、分辨率以及缺损具有更强的鲁棒性,并且配准效率更高。