An easy rapid prototyping technique with point cloud data

Because of its high sensitivity to misalignment, precision grinding of free-form surfaces with micron accuracy requires accurate registration of the surface measurement point cloud. Registration of point clouds obtained with a coordinate measuring machine (CMM) is generally an iterative process of finding optimal coordinate transformation between the CMM frame and the model frame of the workpiece by minimizing the point-to-surface distances with probe radius compensation. For free-form surfaces, frequent calculation of point-to-surface distances consumes very much time, and a trade-off has to be made between the efficiency and the accuracy. This paper presents a method for fast registration of free-form surface point clouds based on the point-to-triangle distance which involves only Delaunay triangulation of a two-dimensional dataset, and the surface normal is quickly calculated from cross product. Probe radius compensation is realized by registering the probe center points with the offset surface. We prove that it is equivalent to registering the probe contact points with the nominal surface through theoretical analysis. The registration problem is then formulated as sequential linear least-square problems with properly defined ball constraints. To validate the method, numerical simulations are presented to show the accuracy of the point-to-triangle distance. The registration algorithm also shows excellent robustness against misalignment of tens of millimeters/degrees. Finally measurement, registration, and grinding of a free-form optical surface are experimentally demonstrated. The surface error obtained after registration is used for compensatory grinding which reduces it to micron level.

Workpiece localization has direct relations with many manufacturing automation applications. In order to gain accurate workpiece measurement by on-machine measurement system or coordinate measuring machines (CMM), the touch trigger probe is widely adopted. In spite of the high repeatability of the touch trigger probe, there are still error sources associated with the probe. In this paper, we will focus on probe radius compensation. We first show the sources of probe radius error. Then, several compensation methods in related papers are reviewed. After that, a new radius compensation method is proposed in this paper. Simulation and experimental results of probe radius compensation by different methods are given. It is shown that our proposed method has the best performance both in terms of compensation accuracy and computational time. The method is also implemented in a computer aided setup (CAS) system.

The cloud computing can allocate computer resources according to customers different demand dynamic status, which has improved the calculation efficiency, and saved the cost of hardware. This paper establishes the 3D mathematical model of data programming tool radius compensation system, and gets the tool radius compensation control equation according to the interpolation function. It designs the tool radius compensation discrimination system with the combination of UG software. In order to verify the validity and reliability of the system, the paper takes the processing process of leaf as an example to design the tool radius compensation algorithm, and the compensation deviation has been made numerical simulation, which has obtained error compensation curve and tool radius compensation parameter table. It can obtain reasonable tool compensation cutter and cycle according to the input of tool parameters, which provides technical support for the research of numerical control (NC) machining technology.

Tool compensation determines the machining precision and quality . There are tool length compensation,tool radius compensation and corner radius compensation in CNC system .The parameters of these compensation are all static.The purpose of this study was that the machining precision and quality were improved by changing the parameters of tool compensation,based upon changing the static parameters into the dynamic parameters of tool compensation.The three tool compensations were introduced and discussed about these being used in machining process and the function in processing.The concept of dynamic tool compensation was proposed in this paper. The method was used that the dynamic parameter substituted the static parameter in the processing by calculating . The process machining crafts information was included in tool compensation in every process step. The computer program was finished about how to calculate the dynamic parameter. The results indicate that high-precision machining is within the range of the maximum probability and the large machining error is within the range of the minimum probability by useing this method. Further more, the end qualified product is more than before.Process indicators and tool compensation merged is a breakthrough research by probability calculating for impoving the machining precision.We confirmed that the proposed method could be l used widely.

This paper presents a novel approach to construct spatially-referenced, multidimensional virtual city models from remotely generated point clouds for areas that lack reliable geographical reference data. A multidimensional point cloud is an unstructured array of single, irregular points in a spatial 3D coordinate system plus time stamp. If geospatial reference points are available, a point cloud is geo-referenced. Geo-referenced point clouds contain a high-precision reference dataset. Point clouds can be utilised in a variety of applications. They are particularly suitable for the representation of surfaces, structures, terrain and objects. Point clouds are used here to generate a virtual 3D city model representing the complex, granular cityscape of Jerusalem and its centre, the Old City. The generation of point clouds is based on two data acquisition methods: active data capture by laser scanning and passive data collection by photogrammetric methods. In our case, very high-resolution stereo imagery in visible light and near infrared bands have been systematically acquired an aerial flight campaign. The spatio-temporal data gathered necessitate further processing to extract the geographical reference and semantic features required in a specific resolution and scale. An insight is given into the processing of an unstructured point cloud to extract and classify the 3D urban fabric and reconstruct its objects. Eventually, customised, precise and up-to-date geographical datasets can be made available for a defined region at a defined resolution and scale.

Abstract. The aim of the research is to evaluate the performance of the point cloud registration methods using mobile laser scanning data. The point cloud registration methods involved in this research are match bounding-box centres and iterative closest point (ICP). The research began with the two epoch’s mobile laser scanning survey using a Phoenix AL-3-32 system. At the same time, the stereo images of the study area were acquired using UAV Photogrammetric method. Both two epoch point cloud datasets were gone through the pre and post-processing stages to produce the cleaned and geo-referenced point clouds data. The data were then gone through the two registration methods and four Cloud-to-Cloud (C2C) distance methods. The 3D surface deviation results obtained from mobile laser scanning data was compared with the 3D surface deviation results from UAV data that undergoes the same registration and C2C distance computation methods. The study area involved in the research is an active landslide area that was located at Kulim Hi-Tech residential area in Kedah state, Malaysia. The study area exposed to the movement of the land which caused cracked to the buildings and drainages. The findings show that the ICP registration becomes the most suitable method to register point clouds dataset that was acquired using mobile laser scanning system. Among the four C2C distance computation methods that was involved in the testing, the least square plane method was the best method to calculate the distance between two sets of point clouds datasets which in turn gave the best results in the process of detecting the movement of the land in the study area.

This study investigates the capture of digital data and the development of models for structures with incomplete documentation and plans. LiDAR technology is utilized to obtain the point clouds of a pedestrian bridge structure. Two different point clouds with varying densities, (i) fine (11 collection locations) and (ii) coarse (4 collection locations), collected via terrestrial LiDAR, are analyzed to generate geometry and structural sections. This geometry is compared to the structural plans, which are then converted into numerical models (finite element—FE model) based on the point cloud data. Point cloud-based FE models (based on fine and coarse data) are compared with the structural plan-based FE model. It is observed that the static and dynamic responses are comparable within an acceptable range of a maximum difference of 5.5% for static deformation and an 8.23% frequency difference, with an average difference of less than 5%. Additionally, the dynamic properties of the fine and coarse point cloud FE models are compared with the operational modal analysis data obtained from the bridge. The fine and course point-cloud-based FE models, without model calibration, achieve an average accuracy of 8.76% and 9.94% for natural frequencies and a 0.89 modal assurance criterion value. The research found that the digital data generation yields promising results in this case for a bridge if documentation or plans are unavailable. With recent technologies and approaches such as digital twins, the connection between physical and virtual entities needs to be established by fusing digital models, sensorial information, and other data forms for better infrastructure management. Models such as those investigated and discussed in this paper can assist engineers with structural preservation in conjunction with monitoring data and utilization for digital twins.

Accurate dimensional measurement of freeform surfaces is a key step in CNC machining. Usually, a CMM equipped with a touch trigger probe is employed for measurement of such surfaces. In such a precision measurement, probe radius error is a serious concern. Traditionally, probe radius compensation methods are based on using the design (CAD) surface. In specific instances, the inaccuracy of these compensation techniques could be one order magnitude higher than the repeatability of the probe. Probe radius compensation based on ideal machined surface (generated when the milling cutter accurately moves through the cutter location (CL) points) is superior in principle to the one based on design surface, since it is one level of abstraction closer to the final surface realized. On Machine Measurement (OMM), where the measurement process is accomplished on the machining center by replacing the milling cutter with the Touch Trigger Probe, affords such a possibility without resorting to computationally intensive surface localization. This paper presents the implementation and performance analysis of a proposed new probe radius compensation technique based on both the design surface data (CAD model) and machined surface data (CL points). The proposed method efficiently compensates the probe radius error of on-machine measurement data for sculptured surfaces. The model was implemented on the simulated CMM data of a bi-cubic Bezier surface machined by a ball-end mill and compared with the conventional compensation techniques. The results show that this method compensates the probe radius error better than the existing techniques and yields superior precision.

At present, 3D vision technology has been more and more applied in the field of livestock breading industry. Three depth cameras are installed in the data acquisition walkway from different views, as the animals pass through the walkway, the depth cameras will get the local point cloud of livestock at the same time. By this method the livestock point cloud exists inevitable noise, as well as the background point cloud including railing and ground. Therefore, the segmentation of livestock point cloud from the original point cloud mixed with noise and background is the important first step of the application of three-dimensional vision in livestock breading industry. This paper proposed using the PointNet++ model to separate the target point cloud from the background point cloud. The improved DBSCAN clustering algorithm is proposed to denoise and segment the data preliminarily, which is helpful to label the data manually. The labeled data are used to train the PointNet++ neural network model to extract target point cloud efficiently and accurately. The experimental results show that the mean Intersection over Union (MIOU) of pig target point cloud segmentation based on single-scale grouping and multi-scale grouping Pointnet++ are 0.932 and 0.940, respectively, which both obtain good segmentation results.

Multi-temporal digital terrain models (DTMs) derived from airborne or uncrewed aerial vehicle (UAV)-borne light detection and ranging (LiDAR) platforms are frequently used tools in geomorphic impact studies. Accurate estimation of mobilized sediments from multi-temporal DTMs is indispensable for hazard assessment. To study volumetric changes in alpine environments it is crucial to identify and discuss different kind of error sources in multi-temporal data. We subdivided errors into those caused by data acquisition, data processing, and spatial properties of the terrain. In terms of the quantification of surface changes, the propagation of errors can lead to high uncertainties. Three alpine catchments with different LiDAR point clouds of different origins (airborne laser scanning [ALS], UAV-borne laser scanning [ULS]), varying point densities, accuracies and qualities were analysed, and used as basis for interpolating DTMs. The workflow was developed in the Schöttlbach area in Styria and later applied to further catchments in Austria. The main aim of the presented work is a comprehensive DTM uncertainty analysis specially designed for geomorphic impact studies, with a resulting uncertainty analysis serving as input for a change detection tool. Our findings reveal that geomorphic impact studies need the careful distinction between actual surface changes and different data uncertainties. ULS combines the benefits of terrestrial laser scanning with all the benefits of ALS. However, the use of ULS data does not necessarily improve the results of the analysis since the high level of detail is not always helpful in geomorphic impact studies. In order to make the different point clouds and DTMs comparable the quality of the ULS point cloud had to be reduced to fit the accuracy of the reference data (older ALS point clouds). Using a point cloud with a high point density with a regular planimetric point spacing and less data gaps, in the best case collected during leaf-off conditions (e.g., cross-flight strategy) turned out to be sufficient for our geomorphic research purposes.

In view of the complex development process of mechanical equipment and high research and development costs, the virtual assembly technology can quickly and effectively discover the problems and risks that may occur in the assembly process of the structure. A set of general schemes for the reconstruction and updating of assembly geometric models, which are comprehensively used by three modeling methods: parametric modeling, synchronous modeling and reverse engineering modeling, are given. Taking a surface block structure with free-form surface as the measurement object, the surface of the surface block generated by 3D printing is scanned by laser, and the k-d tree topological relationship is established for the obtained point cloud data. Combining the search strategy based on k-d tree and ICP algorithm, the point cloud data scanned from different angles in the same area are spliced. Through the analysis of the surface point cloud data model of the surface block, the spatial point cloud data is mapped to the two-dimensional plane by using the projection plane method to realize triangulation, and finally complete the surface reconstruction of the surface block.

With the popularization and use of three-dimensional data acquisition equipment, the acquisition of three-dimensional data is more convenient. Three-dimensional data contains rich shape and scale information. How to effectively and accurately classify, segment and identify three-dimensional data is a research hotspot in the field of computer vision. Aiming at the particularity of 3D point cloud data and the neglect of local correlation between points, this paper proposes a new end-to-end depth network framework, namely KE-PointVNet. KE-PointVNet can directly deal with point cloud and construct deep convolutional network. KE-PointVNet extracts local geometric features of each point based on the EdgeConv module. By using the KNN proximity algorithm to construct local neighborhood of the point cloud, it can avoid its non-local diffusion, and finally obtain the high-level semantics of the point cloud through the local aggregation vector VLAD layer. The experimental results show that this method has a higher classification accuracy than most of the existing point cloud classification methods.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

The expansion of tunnel scale has led to a massive demand for inspections. Light Detection And Ranging (LiDAR) has been widely applied in tunnel structure inspections due to its fast data acquisition speed and strong environmental adaptability. However, raw tunnel point-cloud data contain noise point clouds, such as non-structural facilities, which affect the detection of tunnel lining structures. Methods such as point-cloud filtering and machine learning have been applied to tunnel point-cloud denoising, but these methods usually require a lot of manual data preprocessing. In order to directly denoise the tunnel point cloud without preprocessing, this study proposes a comprehensive processing method for cross-section fitting and point-cloud denoising in subway shield tunnels based on the Huber loss function. The proposed method is compared with classical fitting denoising methods such as the least-squares method and random sample consensus (RANSAC). This study is experimentally verified with 40 m long shield-tunnel point-cloud data. Experimental results show that the method proposed in this study can more accurately fit the geometric parameters of the tunnel lining structure and denoise the point-cloud data, achieving a better denoising effect. Meanwhile, since coordinate system transformations are required during the point-cloud denoising process to handle the data, manual rotations of the coordinate system can introduce errors. This study simultaneously combines the Huber loss function with principal component analysis (PCA) and proposes a three-dimensional spatial coordinate system transformation method for tunnel point-cloud data based on the characteristics of data distribution.

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.