Journal articles on the topic '3D point cloud modeling'
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Özdemir, E., and F. Remondino. "SEGMENTATION OF 3D PHOTOGRAMMETRIC POINT CLOUD FOR 3D BUILDING MODELING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W10 (September 12, 2018): 135–42. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w10-135-2018.
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<p><strong>Abstract.</strong> 3D city modeling has become important over the last decades as these models are being used in different studies including, energy evaluation, visibility analysis, 3D cadastre, urban planning, change detection, disaster management, etc. Segmentation and classification of photogrammetric or LiDAR data is important for 3D city models as these are the main data sources, and, these tasks are challenging due to their complexity. This study presents research in progress, which focuses on the segmentation and classification of 3D point clouds and orthoimages to generate 3D urban models. The aim is to classify photogrammetric-based point clouds (&gt;<span class="thinspace"></span>30<span class="thinspace"></span>pts/sqm) in combination with aerial RGB orthoimages (~<span class="thinspace"></span>10<span class="thinspace"></span>cm, RGB image) in order to name buildings, ground level objects (GLOs), trees, grass areas, and other regions. If on the one hand the classification of aerial orthoimages is foreseen to be a fast approach to get classes and then transfer them from the image to the point cloud space, on the other hand, segmenting a point cloud is expected to be much more time consuming but to provide significant segments from the analyzed scene. For this reason, the proposed method combines segmentation methods on the two geoinformation in order to achieve better results.</p>
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Yang, Zeyin. "3D Modeling of Sculpture Nano-Ceramics under Sparse Image Sequence." International Journal of Analytical Chemistry 2022 (July 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/5710535.
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To improve the analysis ability of point cloud 3D reconstruction of sparse images of nano-ceramic sculpture points, an automatic cloud 3D reconstruction method of nano-ceramic sculpture points based on sparse image sequence is proposed. Firstly, 3D angle detection and edge contour feature extraction methods are used to analyze 3D point cloud features of nano-ceramic sculpture point save image; secondly, the point cloud of the fuel economy image of nano-ceramic sculpture points is merged and the sloping action method is used to shape degradation to realize the information increase and fusion filtering of the fuel economy image of nano-ceramic sculpture points; finally, combined with the local mean denoising method, image is refined to improve the ability of sparse image outline structure of nano-ceramic sculpture points. The simulation results show that this method has high accuracy, good image matching ability, and high signal-to-noise ratio.
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Gong, Jingyu, Zhou Ye, and Lizhuang Ma. "Neighborhood co-occurrence modeling in 3D point cloud segmentation." Computational Visual Media 8, no. 2 (December 6, 2021): 303–15. http://dx.doi.org/10.1007/s41095-021-0244-6.
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AbstractA significant performance boost has been achieved in point cloud semantic segmentation by utilization of the encoder-decoder architecture and novel convolution operations for point clouds. However, co-occurrence relationships within a local region which can directly influence segmentation results are usually ignored by current works. In this paper, we propose a neighborhood co-occurrence matrix (NCM) to model local co-occurrence relationships in a point cloud. We generate target NCM and prediction NCM from semantic labels and a prediction map respectively. Then, Kullback-Leibler (KL) divergence is used to maximize the similarity between the target and prediction NCMs to learn the co-occurrence relationship. Moreover, for large scenes where the NCMs for a sampled point cloud and the whole scene differ greatly, we introduce a reverse form of KL divergence which can better handle the difference to supervise the prediction NCMs. We integrate our method into an existing backbone and conduct comprehensive experiments on three datasets: Semantic3D for outdoor space segmentation, and S3DIS and ScanNet v2 for indoor scene segmentation. Results indicate that our method can significantly improve upon the backbone and outperform many leading competitors.
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Wu, Youping, and Zhihui Zhou. "Intelligent City 3D Modeling Model Based on Multisource Data Point Cloud Algorithm." Journal of Function Spaces 2022 (July 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/6135829.
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With the rapid development of smart cities, intelligent navigation, and autonomous driving, how to quickly obtain 3D spatial information of urban buildings and build a high-precision 3D fine model has become a key problem to be solved. As the two-dimensional mapping results have constrained various needs in people’s social life, coupled with the concept of digital city and advocacy, making three-dimensional, virtualization and actualization become the common pursuit of people’s goals. However, the original point cloud obtained is always incomplete due to reasons such as occlusion during acquisition and data density decreasing with distance, resulting in extracted boundaries that are often incomplete as well. In this paper, based on the study of current mainstream 3D model data organization methods, geographic grids and map service specifications, and other related technologies, an intelligent urban 3D modeling model based on multisource data point cloud algorithm is designed for the two problems of unified organization and expression of urban multisource 3D model data. A point cloud preprocessing process is also designed: point cloud noise reduction and downsampling to ensure the original point cloud geometry structure remain unchanged, while improving the point cloud quality and reducing the number of point clouds. By outputting to a common 3D format, the 3D model constructed in this paper can be applied to many fields such as urban planning and design, architectural landscape design, urban management, emergency disaster relief, environmental protection, and virtual tourism.
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Nakagawa, M., T. Yamamoto, S. Tanaka, M. Shiozaki, and T. Ohhashi. "TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W5 (May 11, 2015): 13–18. http://dx.doi.org/10.5194/isprsarchives-xl-4-w5-13-2015.
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We focus on a region-based point clustering to extract a polygon from a massive point cloud. In the region-based clustering, RANSAC is a suitable approach for estimating surfaces. However, local workspace selection is required to improve a performance in a surface estimation from a massive point cloud. Moreover, the conventional RANSAC is hard to determine whether a point lies inside or outside a surface. In this paper, we propose a method for panoramic rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point cloud clustering in modeling after point cloud registration. First, we propose a point cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point cloud from some viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point cloud clustering from a complex indoor environment.
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Sanchez, J., F. Denis, F. Dupont, L. Trassoudaine, and P. Checchin. "DATA-DRIVEN MODELING OF BUILDING INTERIORS FROM LIDAR POINT CLOUDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 395–402. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-395-2020.
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Abstract. This paper deals with 3D modeling of building interiors from point clouds captured by a 3D LiDAR scanner. Indeed, currently, the building reconstruction processes remain mostly manual. While LiDAR data have some specific properties which make the reconstruction challenging (anisotropy, noise, clutters, etc.), the automatic methods of the state-of-the-art rely on numerous construction hypotheses which yield 3D models relatively far from initial data. The choice has been done to propose a new modeling method closer to point cloud data, reconstructing only scanned areas of each scene and excluding occluded regions. According to this objective, our approach reconstructs LiDAR scans individually using connected polygons. This modeling relies on a joint processing of an image created from the 2D LiDAR angular sampling and the 3D point cloud associated to one scan. Results are evaluated on synthetic and real data to demonstrate the efficiency as well as the technical strength of the proposed method.
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Liu, Shan, Guanghong Gong, Luhao Xiao, Mengyuan Sun, and Zhengliang Zhu. "Study of rapid face modeling technology based on Kinect." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 01 (January 23, 2018): 1750054. http://dx.doi.org/10.1142/s1793962317500544.
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This paper improves the algorithm of point cloud filtering and registration in 3D modeling, aiming for smaller sampling error and shorter processing time of point cloud data. Based on collaborative sampling among several Kinect devices, we analyze the deficiency of current filtering algorithm, and use a novel method of point cloud filtering. Meanwhile, we use Fast Point Feature Histogram (FPFH) algorithm for feature extraction and point cloud registration. Compared with the aligning process using Point Feature Histograms (PFH), it only takes 9[Formula: see text]min when the number of points is about 500,000, shortening the aligning time by 47.1%. To measure the accuracy of the registration, we propose an algorithm which calculates the average distance of the corresponding coincident parts of two point clouds, and we improve the accuracy to an average distance of 0.7[Formula: see text]mm. In the surface reconstruction section, we adopt Ball Pivoting algorithm for surface reconstruction, obtaining image with higher accuracy in a shorter time.
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Zainuddin, K., Z. Majid, M. F. M. Ariff, K. M. Idris, M. A. Abbas, and N. Darwin. "3D MODELING FOR ROCK ART DOCUMENTATION USING LIGHTWEIGHT MULTISPECTRAL CAMERA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 787–93. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-787-2019.
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<p><strong>Abstract.</strong> This paper discusses the use of the lightweight multispectral camera to acquire three-dimensional data for rock art documentation application. The camera consists of five discrete bands, used for taking the motifs of the rock art paintings on a big structure of a cave based on the close-range photogrammetry technique. The captured images then processed using commercial structure-from-motion photogrammetry software, which automatically extracts the tie point. The extracted tie points were then used as input to generate a dense point cloud based on the multi-view stereo (MVS) and produced the multispectral 3D model, and orthophotos in a different wavelength. For comparison, the paintings and the wall surface also observed by using terrestrial laser scanner which capable of recording thousands of points in a short period of time with high accuracy. The cloud-to-cloud comparison between multispectral and TLS 3D point cloud show a sub-cm discrepancy, considering the used of the natural features as control target during 3D construction. Nevertheless, the processing also provides photorealistic orthophoto, indicates the advantages of the multispectral camera in generating dense 3D point cloud as TLS, photorealistic 3D model as RGB optic camera, and also with the multiwavelength output.</p>
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Chibunichev, A. G., and V. P. Galakhov. "IMAGE TO POINT CLOUD METHOD OF 3D-MODELING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B3 (July 23, 2012): 13–16. http://dx.doi.org/10.5194/isprsarchives-xxxix-b3-13-2012.
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Hsieh, Chia-Sheng, and Xiang-Jie Ruan. "Automated Semantic Segmentation of Indoor Point Clouds from Close-Range Images with Three-Dimensional Deep Learning." Buildings 13, no. 2 (February 9, 2023): 468. http://dx.doi.org/10.3390/buildings13020468.
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The creation of building information models requires acquiring real building conditions. The generation of a three-dimensional (3D) model from 3D point clouds involves classification, outline extraction, and boundary regularization for semantic segmentation. The number of 3D point clouds generated using close-range images is smaller and tends to be unevenly distributed, which is not conducive to automated modeling processing. In this paper, we propose an efficient solution for the semantic segmentation of indoor point clouds from close-range images. A 3D deep learning framework that achieves better results is further proposed. A dynamic graph convolutional neural network (DGCNN) 3D deep learning method is used in this study. This method was selected to learn point cloud semantic features. Moreover, more efficient operations can be designed to build a module for extracting point cloud features such that the problem of inadequate beam and column classification can be resolved. First, DGCNN is applied to learn and classify the indoor point cloud into five categories: columns, beams, walls, floors, and ceilings. Then, the proposed semantic segmentation and modeling method is utilized to obtain the geometric parameters of each object to be integrated into building information modeling software. The experimental results show that the overall accuracy rates of the three experimental sections of Area_1 in the Stanford 3D semantic dataset test results are 86.9%, 97.4%, and 92.5%. The segmentation accuracy of corridor 2F in a civil engineering building is 94.2%. In comparing the length with the actual on-site measurement, the root mean square error is found to be ±0.03 m. The proposed method is demonstrated to be capable of automatic semantic segmentation from 3D point clouds with indoor close-range images.
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Pu, Xinming, Shu Gan, Xiping Yuan, and Raobo Li. "Feature Analysis of Scanning Point Cloud of Structure and Research on Hole Repair Technology Considering Space-Ground Multi-Source 3D Data Acquisition." Sensors 22, no. 24 (December 8, 2022): 9627. http://dx.doi.org/10.3390/s22249627.
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As one of the best means of obtaining the geometry information of special shaped structures, point cloud data acquisition can be achieved by laser scanning or photogrammetry. However, there are some differences in the quantity, quality, and information type of point clouds obtained by different methods when collecting point clouds of the same structure, due to differences in sensor mechanisms and collection paths. Thus, this study aimed to combine the complementary advantages of multi-source point cloud data and provide the high-quality basic data required for structure measurement and modeling. Specifically, low-altitude photogrammetry technologies such as hand-held laser scanners (HLS), terrestrial laser scanners (TLS), and unmanned aerial systems (UAS) were adopted to collect point cloud data of the same special-shaped structure in different paths. The advantages and disadvantages of different point cloud acquisition methods of special-shaped structures were analyzed from the perspective of the point cloud acquisition mechanism of different sensors, point cloud data integrity, and single-point geometric characteristics of the point cloud. Additionally, a point cloud void repair technology based on the TLS point cloud was proposed according to the analysis results. Under the premise of unifying the spatial position relationship of the three point clouds, the M3C2 distance algorithm was performed to extract the point clouds with significant spatial position differences in the same area of the structure from the three point clouds. Meanwhile, the single-point geometric feature differences of the multi-source point cloud in the area with the same neighborhood radius was calculated. With the kernel density distribution of the feature difference, the feature points filtered from the HLS point cloud and the TLS point cloud were fused to enrich the number of feature points in the TLS point cloud. In addition, the TLS point cloud voids were located by raster projection, and the point clouds within the void range were extracted, or the closest points were retrieved from the other two heterologous point clouds, to repair the top surface and façade voids of the TLS point cloud. Finally, high-quality basic point cloud data of the special-shaped structure were generated.
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Zhang, Yang, Zhen Liu, Xiang Li, and Yu Zang. "Data-Driven Point Cloud Objects Completion." Sensors 19, no. 7 (March 28, 2019): 1514. http://dx.doi.org/10.3390/s19071514.
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With the development of the laser scanning technique, it is easier to obtain 3D large-scale scene rapidly. However, many scanned objects may suffer serious incompletion caused by the scanning angles or occlusion, which has severely impacted their future usage for the 3D perception and modeling, while traditional point cloud completion methods often fails to provide satisfactory results due to the large missing parts. In this paper, by utilising 2D single-view images to infer 3D structures, we propose a data-driven Point Cloud Completion Network ( P C C N e t ), which is an image-guided deep-learning-based object completion framework. With the input of incomplete point clouds and the corresponding scanned image, the network can acquire enough completion rules through an encoder-decoder architecture. Based on an attention-based 2D-3D fusion module, the network is able to integrate 2D and 3D features adaptively according to their information integrity. We also propose a projection loss as an additional supervisor to have a consistent spatial distribution from multi-view observations. To demonstrate the effectiveness, first, the proposed P C C N e t is compared to recent generative networks and has shown more powerful 3D reconstruction abilities. Then, P C C N e t is compared to a recent point cloud completion methods, which has demonstrate that the proposed P C C N e t is able to provide satisfied completion results for objects with large missing parts.
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Ma, Dong Ling, Jian Cui, and Fei Cai. "Three Dimensional Modeling of Buildings Using Three Dimensional Laser Scanning Data." Advanced Materials Research 594-597 (November 2012): 2398–401. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.2398.
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This paper provides a scheme to construct three dimensional (3D) model fast using laser scanning data. In the approach, firstly, laser point cloud are scanned from different scan positions and the point cloud coming from neighbor scan stations are spliced automatically to combine a uniform point cloud model, and then feature lines are extracted through the point cloud, and the framework of the building are extracted to generate 3D models. At last, a conclusion can be drawn that 3D visualization model can be generated quickly using 3D laser scanning technology. The experiment result shows that it will bring the application model and technical advantage which traditional mapping way can not have.
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Pružinec, Filip, and Renata Ďuračiová. "A Point-Cloud Solar Radiation Tool." Energies 15, no. 19 (September 24, 2022): 7018. http://dx.doi.org/10.3390/en15197018.
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Current software solutions for solar-radiation modeling in 3D focus on the urban environment. Most of the published tools do not implement methods to consider complex objects, such as urban greenery in their models or they expect a rather complex 3D mesh to represent such objects. Their use in an environment that is difficult to represent geometrically, such as vegetation-covered areas, is rather limited. In this paper, we present a newly developed solar-radiation tool focused on solar-radiation modeling in areas with complex objects, such as vegetation. The tool uses voxel representations of space based on point-cloud data to calculate the illumination and ESRA solar-radiation model to estimate the direct, diffuse, and global irradiation in a specified time range. We demonstrate the capabilities of this tool on a forested mountain area of Suchá valley in the Hight Tatra mountains (Slovakia) and also in the urban environment of Castle Hill in Bratislava (Slovakia) with urban greenery. We compare the tool with the r.sun module of GRASS GIS and the Area Solar Radiation tool of ArcGIS using point-cloud data generated from the digital-terrain model of Kamenistá valley in High Tatra mountains in Slovakia. The results suggest a higher detail of the model in rugged terrain and comparable results on smooth surfaces when considering its purpose as a 3D modeling tool. The performance is tested using different hardware and input data. The processing times are less than 8 min, and 8 GB of memory is used with 4 to 16 core processors and point clouds larger than 100,000 points. The tool is, therefore, easily usable on common computers.
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Malinverni, E. S., R. Pierdicca, M. Paolanti, M. Martini, C. Morbidoni, F. Matrone, and A. Lingua. "DEEP LEARNING FOR SEMANTIC SEGMENTATION OF 3D POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 23, 2019): 735–42. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-735-2019.
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<p><strong>Abstract.</strong> Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great variety in their nature, size and complexity; from small artefacts and museum items to cultural landscapes, from historical building and ancient monuments to city centers and archaeological sites. Cultural Heritage around the globe suffers from wars, natural disasters and human negligence. The importance of digital documentation is well recognized and there is an increasing pressure to document our heritage both nationally and internationally. For this reason, the three-dimensional scanning and modeling of sites and artifacts of cultural heritage have remarkably increased in recent years. The semantic segmentation of point clouds is an essential step of the entire pipeline; in fact, it allows to decompose complex architectures in single elements, which are then enriched with meaningful information within Building Information Modelling software. Notwithstanding, this step is very time consuming and completely entrusted on the manual work of domain experts, far from being automatized. This work describes a method to label and cluster automatically a point cloud based on a supervised Deep Learning approach, using a state-of-the-art Neural Network called PointNet++. Despite other methods are known, we have choose PointNet++ as it reached significant results for classifying and segmenting 3D point clouds. PointNet++ has been tested and improved, by training the network with annotated point clouds coming from a real survey and to evaluate how performance changes according to the input training data. It can result of great interest for the research community dealing with the point cloud semantic segmentation, since it makes public a labelled dataset of CH elements for further tests.</p>
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Van Nguyen, Sinh, Ha Manh Tran, and Minh Khai Tran. "An Improved Method for Restoring the Shape of 3D Point Cloud Surfaces." International Journal of Synthetic Emotions 9, no. 2 (July 2018): 37–53. http://dx.doi.org/10.4018/ijse.2018070103.
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Building 3D objects or reconstructing their surfaces from 3D point cloud data are researched activities in the field of geometric modeling and computer graphics. In the recent years, they are also studied and used in some fields such as: graph models and simulation; image processing or restoration of digital heritages. This article presents an improved method for restoring the shape of 3D point cloud surfaces. The method is a combination of creating a Bezier surface patch and computing tangent plane of 3D points to fill holes on a surface of 3D point clouds. This method is described as follows: at first, a boundary for each hole on the surface is identified. The holes are then filled by computing Bezier curves of surface patches to find missing points. After that, the holes are refined based on two steps (rough and elaborate) to adjust the inserted points and preserve the local curvature of the holes. The contribution of the proposed method has been shown in processing time and the novelty of combined computation in this method has preserved the initial shape of the surface
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Sarakinou, I., K. Papadimitriou, O. Georgoula, and P. Patias. "UNDERWATER 3D MODELING: IMAGE ENHANCEMENT AND POINT CLOUD FILTERING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 441–47. http://dx.doi.org/10.5194/isprs-archives-xli-b2-441-2016.
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This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images’ radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.
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Sarakinou, I., K. Papadimitriou, O. Georgoula, and P. Patias. "UNDERWATER 3D MODELING: IMAGE ENHANCEMENT AND POINT CLOUD FILTERING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 441–47. http://dx.doi.org/10.5194/isprsarchives-xli-b2-441-2016.
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This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images’ radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.
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Wu, Lu Lu, Ying Chen, Zhong Ke Feng, Xue Hai Tang, Zhuo Xu, and Fei Yan. "Research on Trunk Modeling Based on 3D Laser Scanning." Key Engineering Materials 467-469 (February 2011): 1674–79. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1674.
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This Point cloud data of trees extracted from 3D laser scanning were used to do analysis and research on the 3D modeling of trunks. The software of Geomagic Studio was used to separate and eliminate the noises of point cloud of trees, to acquire the point cloud data of trunks. On the basis of this, the data was resampled and modeled by different encapsulation methods. The result demonstrates that the model got by the method of maximal distance is the best.
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Yi, Cheng, Dening Lu, Qian Xie, Shuya Liu, Hu Li, Mingqiang Wei, and Jun Wang. "Hierarchical tunnel modeling from 3D raw LiDAR point cloud." Computer-Aided Design 114 (September 2019): 143–54. http://dx.doi.org/10.1016/j.cad.2019.05.033.
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Yang, Jianjian, Chao Wang, Wenjie Luo, Yuchen Zhang, Boshen Chang, and Miao Wu. "Research on Point Cloud Registering Method of Tunneling Roadway Based on 3D NDT-ICP Algorithm." Sensors 21, no. 13 (June 29, 2021): 4448. http://dx.doi.org/10.3390/s21134448.
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In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.
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Nikoohemat, S., A. Diakité, S. Zlatanova, and G. Vosselman. "INDOOR 3D MODELING AND FLEXIBLE SPACE SUBDIVISION FROM POINT CLOUDS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 285–92. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-285-2019.
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<p><strong>Abstract.</strong> Indoor navigation can be a tedious process in a complex and unknown environment. It gets more critical when the first responders try to intervene in a big building after a disaster has occurred. For such cases, an accurate map of the building is among the best supports possible. Unfortunately, such a map is not always available, or generally outdated and imprecise, leading to error prone decisions. Thanks to advances in the laser scanning, accurate 3D maps can be built in relatively small amount of time using all sort of laser scanners (stationary, mobile, drone), although the information they provide is generally an unstructured point cloud. While most of the existing approaches try to extensively process the point cloud in order to produce an accurate architectural model of the scanned building, similar to a Building Information Model (BIM), we have adopted a space-focused approach. This paper presents our framework that starts from point-clouds of complex indoor environments, performs advanced processes to identify the 3D structures critical to navigation and path planning, and provides fine-grained navigation networks that account for obstacles and spatial accessibility of the navigating agents. The method involves generating a volumetric-wall vector model from the point cloud, identifying the obstacles and extracting the navigable 3D spaces. Our work contributes a new approach for space subdivision without the need of using laser scanner positions or viewpoints. Unlike 2D cell decomposition or a binary space partitioning, this work introduces a space enclosure method to deal with 3D space extraction and non-Manhattan World architecture. The results show more than 90% of spaces are correctly extracted. The approach is tested on several real buildings and relies on the latest advances in indoor navigation.</p>
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Wang, Hao, Dong Yan Wang, Ting Jian Dong, and Tao Wang. "The Research of Aeroplane Engine Blade 3D Point Clouds Processing." Applied Mechanics and Materials 241-244 (December 2012): 2129–32. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2129.
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This paper made the point cloud data processing for the aircraft engine’s blade. First, collected rough point cloud data by using visual measuring equipment. Then, noise reduced and smoothed, feature detected the point cloud data, took the reasonable simplification, finished pre-processing the point cloud data. Finally, took the surface fitting for the point cloud data after processed. The result proved that processing the point cloud data reduced modeling and machining time, and improved smoothness of the model.
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Semler, Q., D. Suwardhi, E. Alby, A. Murtiyoso, and H. Macher. "REGISTRATION OF 2D DRAWINGS ON A 3D POINT CLOUD AS A SUPPORT FOR THE MODELING OF COMPLEX ARCHITECTURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 26, 2019): 1083–87. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-1083-2019.
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<p><strong>Abstract.</strong> Laser scanning and photogrammetry methods have seen immense development in the last years. From bulky inaccessible systems, these two 3D recording systems has become more or less ubiquitous, which is also the case in the heritage domain. However, automation in point cloud classification and semantic annotation remains a much studied topic. In this paper, an approach to help the classification of point cloud is presented using the help of existing 2D drawings. The 2D drawings are registered unto the 3D data, to then be used as a support in the 3D modeling step. The developed approach includes the computation of the point cloud cross section and detection of feature points. This is then used in a 3D transformation followed by ICP refinement to properly register the vectorized 2D drawing on the 3D point cloud. Results show that the developed algorithm manages to register the 2D drawing automatically and with promising results. The automatically registered 2D drawing, which often times already includes semantic information, was then used to help classify the point cloud into several architectural classes.</p>
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Lee, Hongjae, and Jiyoung Jung. "Clustering-Based Plane Segmentation Neural Network for Urban Scene Modeling." Sensors 21, no. 24 (December 15, 2021): 8382. http://dx.doi.org/10.3390/s21248382.
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Urban scene modeling is a challenging but essential task for various applications, such as 3D map generation, city digitization, and AR/VR/metaverse applications. To model man-made structures, such as roads and buildings, which are the major components in general urban scenes, we present a clustering-based plane segmentation neural network using 3D point clouds, called hybrid K-means plane segmentation (HKPS). The proposed method segments unorganized 3D point clouds into planes by training the neural network to estimate the appropriate number of planes in the point cloud based on hybrid K-means clustering. We consider both the Euclidean distance and cosine distance to cluster nearby points in the same direction for better plane segmentation results. Our network does not require any labeled information for training. We evaluated the proposed method using the Virtual KITTI dataset and showed that our method outperforms conventional methods in plane segmentation. Our code is publicly available.
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Tashi, AMM Sharif Ullah, and Akihiko Kubo. "Geometric Modeling and 3D Printing Using Recursively Generated Point Cloud." Mathematical and Computational Applications 24, no. 3 (September 17, 2019): 83. http://dx.doi.org/10.3390/mca24030083.
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Previous studies have reported that a recursive process called the point cloud creation algorithm (PCA) that generates a point cloud is useful for reverse engineering a planner shape. This study elucidates the characteristics of the parameters used in the recursive process as well as its ability in geometric modeling and 3D printing of 3D shapes. In the recursive process, three constants (center point, initial distance, and initial angle) and two variables (instantaneous distance and instantaneous rotational angle) are employed. The shape-modeling characteristics of the constants and variables are elucidated using some commonly used shapes (straight-line, circle, ellipses, spiral, astroid, S-shape, and leaf-shape). In addition, the shape-modeling capability of the recursive process as a whole is quantified using two parameters called the radius of curvature and aesthetic value. Moreover, an illustrative example that shows the efficacy of the recursive process in virtual and physical prototyping of a relatively complex 3D object is presented. The results show that reverse engineering performed by the recursive-process-created point cloud is free from computational complexity compared to reverse engineering performed by the 3D-scanner-created point cloud. As such, the outcomes of this study enrich the field of reverse engineering.
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Xie, Zhiping, Yancheng Lang, and Luqi Chen. "Geometric Modeling of Rosa roxburghii Fruit Based on Three-Dimensional Point Cloud Reconstruction." Journal of Food Quality 2021 (September 29, 2021): 1–14. http://dx.doi.org/10.1155/2021/9990499.
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Fruit three-dimensional (3D) model is crucial to estimating its geometrical and mechanical properties and improving the level of fruit mechanical processing. Considering the complex geometrical features and the required model accuracy, this paper proposed a 3D point cloud reconstruction method for the Rosa roxburghii fruit based on a three-dimensional laser scanner, including 3D point cloud generation, point cloud registration, fruit thorns segmentation, and 3D reconstruction. The 3D laser scanner was used to obtain the original 3D point cloud data of the Rosa roxburghii fruit, and then the fruit thorns data were removed by the segmentation algorithm combining the statistical outlier removal and radius outlier removal. By analyzing the effects of five-point cloud simplification methods, the optimal simplification method was determined. The Poisson reconstruction algorithm, the screened Poisson reconstruction algorithm, the greedy projection triangulation algorithm, and the Delaunay triangulation algorithm were utilized to reconstruct the fruit model. The number of model vertices, the number of facets, and the relative volume error were used to determine the best reconstruction algorithm. The results indicated that this model can better reconstruct the actual surface of Rosa roxburghii fruit. The method provides a reference for the related application.
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Sahebdivani, Shima, Hossein Arefi, and Mehdi Maboudi. "Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud." Sensors 20, no. 18 (September 13, 2020): 5220. http://dx.doi.org/10.3390/s20185220.
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The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.
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Astuti, Ika Asti, and Ahmad Subekti. "APPLICATION LIDAR AND POINT CLOUDS FOR 3D MODELING OF MUSEUM OBJECT." Jurnal Riset Informatika 4, no. 4 (September 6, 2022): 385–90. http://dx.doi.org/10.34288/jri.v4i4.436.
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A museum is an institution intended for the general public that collects, cares for, presents, and preserves the community's cultural heritage for study, research, and pleasure or entertainment. The museum is undoubtedly one of the educational places for the community because it has many historical objects. It provides an opportunity to make the museum a vital place to be developed in a virtual form, such as Augmented Reality or Virtual Reality. However, one problem in developing virtual media is the 3D modeling of objects for interior design in museums. LiDAR (Light Detection And Ranging) is a near real-time 3D positioning technology. A point cloud collects data points in a 3D coordinate system, generally defined by x, y, and z coordinates. Both of these technologies can be used to create 3D object models quickly. The final result of this research is applying LiDAR & point cloud as a 3D modeling technique and assessing the accuracy of using these techniques for 3D modeling of the museum object
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Su, Jingxin, Ryuji Miyazaki, Toru Tamaki, and Kazufumi Kaneda. "3D Modeling of Lane Marks Using a Combination of Images and Mobile Mapping Data." International Journal of Automation Technology 12, no. 3 (May 1, 2018): 386–94. http://dx.doi.org/10.20965/ijat.2018.p0386.
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When we drive a car, the white lines on the road show us where the lanes are. The lane marks act as a reference for where to steer the vehicle. Naturally, in the field of advanced driver-assistance systems and autonomous driving, lane-line detection has become a critical issue. In this research, we propose a fast and precise method that can create a three-dimensional point cloud model of lane marks. Our datasets are obtained by a vehicle-mounted mobile mapping system (MMS). The input datasets include point cloud data and color images generated by laser scanner and CCD camera. A line-based point cloud region growing method and image-based scan-line method are used to extract lane marks from the input. Given a set of mobile mapping data outputs, our approach takes advantage of all important clues from both the color image and point cloud data. The line-based point cloud region growing is used to identify boundary points, which guarantees a precise road surface region segmentation and boundary points extraction. The boundary points are converted into 2D geometry. The image-based scan line algorithm is designed specifically for environments where it is difficult to clearly identify lane marks. Therefore, we use the boundary points acquired previously to find the road surface region from the color image. The experiments show that the proposed approach is capable of precisely modeling lane marks using information from both images and point cloud data.
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Kim, S., H. G. Kim, and T. Kim. "MESH MODELLING OF 3D POINT CLOUD FROM UAV IMAGES BY POINT CLASSIFICATION AND GEOMETRIC CONSTRAINTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 507–11. http://dx.doi.org/10.5194/isprs-archives-xlii-2-507-2018.
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The point cloud generated by multiple image matching is classified as an unstructured point cloud because it is not regularly point spaced and has multiple viewpoints. The surface reconstruction technique is used to generate mesh model using unstructured point clouds. In the surface reconstruction process, it is important to calculate correct surface normals. The point cloud extracted from multi images contains position and color information of point as well as geometric information of images used in the step of point cloud generation. Thus, the surface normal estimation based on the geometric constraints is possible. However, there is a possibility that a direction of the surface normal is incorrectly estimated by noisy vertical area of the point cloud. In this paper, we propose an improved method to estimate surface normals of the vertical points within an unstructured point cloud. The proposed method detects the vertical points, adjust their normal vectors by analyzing surface normals of nearest neighbors. As a result, we have found almost all vertical points through point type classification, detected the points with wrong normal vectors and corrected the direction of the normal vectors. We compared the quality of mesh models generated with corrected surface normals and uncorrected surface normals. Result of comparison showed that our method could correct wrong surface normal successfully of vertical points and improve the quality of the mesh model.
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Guo, Wenbo, and Jun Zhao. "Study on a Compatible Model Combining Point Cloud Model and Digital Elevation Model." Journal of Physics: Conference Series 2224, no. 1 (April 1, 2022): 012086. http://dx.doi.org/10.1088/1742-6596/2224/1/012086.
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Abstract DEM is an important data source to describe the surface morphology, but it is not a real 3D model, which can not meet the requirements of true 3D description under the ground. LIDAR point cloud data is a new true 3D data with high precision and high density. Based on the analysis of the differences between DEM and point cloud data in acquisition method, data structure and model construction, this paper proposes a 3D point set data model based on regular grid 2D data field, as well as the idea of regional modeling, and tests the feasibility of the data model through the upper and lower boundary modeling method. The experiments show that: (1) the 3D point set data model based on regular grid 2D data field is compatible with complete DEM data and simplified point cloud data, and has good expansibility; (2) the newly-built data model can complete the true 3D modeling of simple underground entity with high efficiency when the amount of data is only doubled; (3) The new data model can be generated by inputting DEM data, point cloud data and simplified algorithm of point cloud data under the same coordinate system. It has the potential of large-scale, multi-scale and automatic output processing, and has a good prospect of popularization.
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Liu, Lupeng, Jun Xiao, and Ying Wang. "Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds." Remote Sensing 11, no. 6 (March 15, 2019): 635. http://dx.doi.org/10.3390/rs11060635.
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In the fields of 3D modeling, analysis of discontinuities and engineering calculation, surface extraction is of great importance. The rapid development of photogrammetry and Light Detection and Ranging (LiDAR) technology facilitates the study of surface extraction. Automatic extraction of rock surfaces from 3D rock-mass point clouds also becomes the basis of 3D modeling and engineering calculation of rock mass. This paper presents an automated and effective method for extracting rock surfaces from unorganized rock-mass point clouds. This method consists of three stages: (i) clustering based on voxels; (ii) estimating major orientations based on Gaussian Kernel and (iii) rock surface extraction. Firstly, the two-level spatial grid is used for fast voxelization and segmenting the point cloud into three types of voxels, including coplanar, non-coplanar and sparse voxels. Secondly, the coplanar voxels, rather than the scattered points, are employed to estimate major orientations by using a bivariate Gaussian Kernel. Finally, the seed voxels are selected on the basis of major orientations and the region growing method based on voxels is applied to extract rock surfaces, resulting in sets of surface clusters. The sub-surfaces of each cluster are coplanar or parallel. In this paper, artificial icosahedron point cloud and natural rock-mass point clouds are used for testing the proposed method, respectively. The experimental results show that, the proposed method can effectively and accurately extract rock surfaces in unorganized rock-mass point clouds.
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Mehranfar, M., H. Arefi, and F. Alidoost. "A PROJECTION-BASED RECONSTRUCTION ALGORITHM FOR 3D MODELING OF BRIDGE STRUCTURES FROM DRONE-BASED POINT CLOUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W1-2021 (September 3, 2021): 77–83. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w1-2021-77-2021.
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Abstract. This paper presents a projection-based method for 3D bridge modeling using dense point clouds generated from drone-based images. The proposed workflow consists of hierarchical steps including point cloud segmentation, modeling of individual elements, and merging of individual models to generate the final 3D model. First, a fuzzy clustering algorithm including the height values and geometrical-spectral features is employed to segment the input point cloud into the main bridge elements. In the next step, a 2D projection-based reconstruction technique is developed to generate a 2D model for each element. Next, the 3D models are reconstructed by extruding the 2D models orthogonally to the projection plane. Finally, the reconstruction process is completed by merging individual 3D models and forming an integrated 3D model of the bridge structure in a CAD format. The results demonstrate the effectiveness of the proposed method to generate 3D models automatically with a median error of about 0.025 m between the elements’ dimensions in the reference and reconstructed models for two different bridge datasets.
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Mwangangi, K. K., P. O. Mc’Okeyo, S. J. Oude Elberink, and F. Nex. "EXPLORING THE POTENTIALS OF UAV PHOTOGRAMMETRIC POINT CLOUDS IN FAÇADE DETECTION AND 3D RECONSTRUCTION OF BUILDINGS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 433–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-433-2022.
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Abstract. The use of Airborne Laser Scanner (ALS) point clouds has dominated 3D buildings reconstruction research, thus giving photogrammetric point clouds less attention. Point cloud density, occlusion and vegetation cover are some of the concerns that promote the necessity to understand and question the completeness and correctness of UAV photogrammetric point clouds for 3D buildings reconstruction. This research explores the potentials of modelling 3D buildings from nadir and oblique UAV image data vis a vis airborne laser data. Optimal parameter settings for dense matching and reconstruction are analysed for both UAV image-based and lidar point clouds. This research employs an automatic data driven model approach to 3D building reconstruction. A proper segmentation into planar roof faces is crucial, followed by façade detection to capture the real extent of the buildings’ roof overhang. An analysis of the quality of point density and point noise, in relation to setting parameter indicates that with a minimum of 50 points/m2, most of the planar surfaces are reconstructed comfortably. But for smaller features than dormers on the roof, a denser point cloud than 80 points/m2 is needed. 3D buildings from UAVs point cloud can be improved by enhancing roof boundary by use of edge information from images. It can also be improved by merging the imagery building outlines, point clouds roof boundary and the walls outline to extract the real extent of the building.
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Huang, Ming, Xueyu Wu, Xianglei Liu, Tianhang Meng, and Peiyuan Zhu. "Integration of Constructive Solid Geometry and Boundary Representation (CSG-BRep) for 3D Modeling of Underground Cable Wells from Point Clouds." Remote Sensing 12, no. 9 (May 4, 2020): 1452. http://dx.doi.org/10.3390/rs12091452.
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The preference of three-dimensional representation of underground cable wells from two-dimensional symbols is a developing trend, and three-dimensional (3D) point cloud data is widely used due to its high precision. In this study, we utilize the characteristics of 3D terrestrial lidar point cloud data to build a CSG-BRep 3D model of underground cable wells, whose spatial topological relationship is fully considered. In order to simplify the modeling process, first, point cloud simplification is performed; then, the point cloud main axis is extracted by OBB bounding box, and lastly the point cloud orientation correction is realized by quaternion rotation. Furthermore, employing the adaptive method, the top point cloud is extracted, and it is projected for boundary extraction. Thereupon, utilizing the boundary information, we design the 3D cable well model. Finally, the cable well component model is generated by scanning the original point cloud. The experiments demonstrate that, along with the algorithm being fast, the proposed model is effective at displaying the 3D information of the actual cable wells and meets the current production demands.
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Wang, Hao, Zhi Jun Cai, and Li Wen Wang. "3D Model Reconstruction of the Broken Aeroengine Blade Based on Multi-Scale Genetic Algorithm." Advanced Materials Research 479-481 (February 2012): 2250–54. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.2250.
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This paper made the 3D model reconstruction of the CFM56-5B turban blade. First, collected rough points cloud data by using visual measuring equipment. Then, smoothed and filtered the point cloud data, took the rational simplification, finished pre-processing the point cloud data. Finally, multi-scale genetic algorithm was used for fitting the edge of turban blade, and reconstructed the 3D digital model. The results proved that this method improved smoothness of the model, and reduced time and cost of modeling and machining.
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Gong, Miao, Hao Wang, and Li Wen Wang. "3D Model Reconstruction of the Broken Aeroengine Blade Based on the Laplacian of Guassian Detection." Advanced Materials Research 490-495 (March 2012): 143–46. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.143.
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This paper made the 3D model reconstruction of the J34 turban blade. First, collected rough points cloud data by using visual measuring equipment. Then, smoothed and filtered the point cloud data, took the rational simplification, finished pre-processing the point cloud data. Finally, the Laplacian of Guassian Detection was used for fitting the edge of turban blade, and reconstructed the 3D digital model. The results proved that this method improved smoothness of the model, and reduced time and cost of modeling and machining.
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Mostafavi, A., M. Scaioni, and V. Yordanov. "PHOTOGRAMMETRIC SOLUTIONS FOR 3D MODELING OF CULTURAL HERITAGE SITES IN REMOTE AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 765–72. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-765-2019.
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Abstract. The realistic possibility of using non-metric digital cameras to achieve reliable 3D models has eased the application of photogrammetry in different domains. Documentation, conservation and dissemination of the Cultural Heritage (CH) can be obtained and implemented through virtual copies and replicas. Structure-from-Motion (SfM) photogrammetry has widely proven its impressive potential for image-based 3D reconstruction resulting in great 3D point clouds’ acquisitions but at minimal cost. Images from Unmanned Aerial Vehicles (UAVs) can be also processed within SfM pipeline to obtain point cloud of Cultural Heritage sites in remote regions. Both aerial and terrestrial images can be integrated to obtain a more complete 3D. In this paper, the application of SfM photogrammetry for surveying of the Ziggurat Chogha Zanbil in Iran is presented. Here point clouds have been derived from oblique and nadir photos captured from UAV as well as terrestrial photos. The obtained four point clouds have been compared on the basis of different techniques to highlight differences among them.
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Jing, Li Ming, Hao Wang, and Li Wen Wang. "3D Model Reconstruction of the Broken Aeroengine Blade Based on the Detection Operator." Applied Mechanics and Materials 159 (March 2012): 1–5. http://dx.doi.org/10.4028/www.scientific.net/amm.159.1.
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This paper made the 3D model reconstruction of the V2500 turban blade. First, collected rough points cloud data by using visual measuring equipment. Then, smoothed and filtered the point cloud data, took the rational simplification, finished pre-processing the point cloud data. Finally, the grayscale of space-image algorithm was used for fitting the edge of turban blade, and reconstructed the 3D digital model. The results proved that this method improved smoothness of the model, and reduced time and cost of modeling and machining.
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Kumazaki, R., and Y. Kunii. "APPLICATION OF 3D TREE MODELING USING POINT CLOUD DATA BY TERRESTRIAL LASER SCANNER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 995–1000. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-995-2020.
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Abstract. Constructing 3D models for trees such as those found in Japanese gardens, in which many species exist, requires the generation of tree shapes that combine the characteristics of the tree's species and natural diversity. Therefore, this study proposes a method for constructing a 3D tree model with highly-accurate tree shape reproducibility from tree point cloud data acquired by TLS. As a method, we attempted to construct a 3D tree model using the TreeQSM, which is open source for TLS-QSM method. However, in TreeQSM, since processing is based on the assumption that the tree point cloud consists of data related to trunks and branches, measuring trees in which leaves have fallen is recommended. To solve this problem, we proposed an efficient classification process that mainly uses thresholds for deviation and reflectance, which are the adjunct data of the object that can be acquired by laser measurement. Furthermore, to verify accuracy of the model, position coordinates from the constructed 3D tree model were extracted. The extracted coordinates were compared with the those of the tree point cloud data to clarify the extent to which the 3D tree model was constructed from the tree point cloud data. As a result, the 3D tree model was constructed within the standard deviation of 0.016 m from the tree point cloud data. Therefore, the reproducibility of the tree shape by the TLS-QSM method was also effective in terms of accuracy.
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Liu, Song, and Xiao Yao Xie. "Research on BSP Algorithm of Construction of Large-Scale 3D Point Model." Applied Mechanics and Materials 513-517 (February 2014): 461–65. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.461.
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For the construction of large-scale surface features 3D point model, a large number of point cloud data processing calculations is needed. Previous model construction calculation was treated non-parallel manner successively and mostly with one by one point cloud. This data processing method is complex, low efficiency and requires vast computing resource. Accordance with the BSP parallel computing ideas, we design a point cloud data modeling algorithm based on BSP and build a Hama parallel computing cluster consisted of ordinary PCs. The results indicate that, large-scale 3D point model BSP construction algorithm can improve the efficiency of modeling calculations and reduce computing resources requirements for processing construction computing.
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Ruiz, P. R. S., C. M. Almeida, M. B. Schimalski, V. Liesenberg, and E. A. Mitishita. "TLS AND SHORT-RANGE PHOTOGRAMMETRIC DATA FUSION FOR BUILDINGS 3D MODELING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 279–84. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-279-2021.
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Abstract. The adoption of 3D survey techniques is essential to promote efficient and timely information acquisition on constructed buildings. This article addresses terrestrial LiDAR (TLS) and close-range photogrammetric data fusion for the 3D modeling of a building in Level of Detail (LoD) 3. The selected building presents challenging elements for modeling, such as extended curved slabs, external glass walls, recessed facades and diverse roof pitches. It is located on the campus of the Federal University of Paraná (UFPR) in Curitiba, Brazil. The accuracy of the data integration was obtained through the analysis of deviations between the clouds of primary points. The accuracy of the point cloud model was verified by comparing its dimensions with the real dimensions of the building, obtained by means of a handheld laser distance meter (EDM). The results demonstrate that there was a correspondence between the EDM measures and the model, with a satisfactory statistical agreement between the estimated and reference values and a general maximum absolute error of 4.5 cm. The article focuses on the accuracy of point cloud models for the cadastral updating of buildings, providing information for decision making in projects documentation and interventions.
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Oh, Ki Won, and Kang Sun Choi. "Valve Modeling and Model Extraction on 3D Point Cloud data." Journal of the Institute of Electronics and Information Engineers 52, no. 12 (December 25, 2015): 77–86. http://dx.doi.org/10.5573/ieie.2015.52.12.077.
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Chen, Guowei. "Point Cloud Key Point Extraction Algorithm Based on Feature Space Value Filtering." Mobile Information Systems 2022 (August 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/1453537.
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With the rapid development of 3-dimensional (3D) acquisition technology, point clouds have a wide range of application prospects in the fields of computer vision, autonomous driving, and robotics. Point cloud data is widely used in many 3D scenes, and deep learning has become a mainstream research method for classification with the advantages of automatic feature extraction and strong generalization ability. In this paper, a hierarchical key point extraction framework is proposed to solve the problem of modeling the local geometric structure between points. Various point cloud models such as PointNet, PointNet++, and DGCNN are analyzed and their features in local key point are extracted. Based on these analyses, an indexed edge geometric feature spatial value screening neural network (IEGCNN) is proposed. This network extracts features from each point and its neighborhood, calculates the distance between the center point and the points within its neighborhood, and adds the point orientation information to the edge feature spatial value screening network. The relationship between points in the edge network architecture is projected onto a 3D coordinate system and decomposed into three orthogonal bases. The geometric structure between two points is modeled by feature aggregation based on the angle between the edge vector and the base vector and the distance between the center point and the neighboring points. The proposed method has the capability of fast processing of point cloud data by significantly reducing the training and recognition time. The experimental results show that this method achieved high classification accuracy value. This work also provides an idea to solve the problem of real-time target detection network, which has a broad applications prospect in the deployment of movable devices and real-time processing.
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Kartini, Gusti Ayu Jessy, and Naura Dwi Saputri. "3D Modeling of Bosscha Observatory With TLS and UAV Integration Data." Geoplanning: Journal of Geomatics and Planning 9, no. 1 (November 15, 2022): 37–46. http://dx.doi.org/10.14710/geoplanning.9.1.37-46.
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Terrestrial Laser Scanner is a tool capable of generating millions 3D points with mm accuracy, but upper structure is difficult to model. Unmanned Aerial Vehicle is an unmanned aircraft system technology that can provide structural data on buildings. Measurements with one technique can lead to unsatisfactory results, so an integration process is carried out to obtain a more accurate 3D model. The purpose of this research is to see the successful integration of TLS and UAV point cloud data for 3D modeling. The data used is secondary data from previous research. TLS and UAV data were processed with Agisoft Metashape and Cyclone in the same coordinate system. The integration process is carried out by aligning the same point cloud between the two data in CloudCompare with an RMSE of 25.60 mm. Validation is done by comparing the distance between the results of the 3D model with the actual conditions. The integrated 3D model can be implemented for the purposes of Bosscha Observatory 3D modeling.
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Cao, Xian Ge, Jin Ling Yang, Dong Hai Li, Jin Yu Feng, and Xiang Lai Meng. "Design and Test of Subway 3D Point Cloud Data Obtained Based on Ground 3D Laser Scanning Technology." Advanced Materials Research 846-847 (November 2013): 981–85. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.981.
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3D laser scanning technology can fast, efficient and accurate access high-precision measurement of the target point cloud data and provides the necessary conditions for the development of digital measuring. This paper gives an example of Subway and elaborates the method of 3D point cloud data acquisition, data processing and modeling, and verifies the feasibility of 3D visualization of Subway based on 3D laser.
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Zhang, Zongliang, Jonathan Li, Xin Li, Yangbin Lin, Shanxin Zhang, and Cheng Wang. "A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 725–28. http://dx.doi.org/10.5194/isprs-archives-xli-b1-725-2016.
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This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.
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Zhang, Zongliang, Jonathan Li, Xin Li, Yangbin Lin, Shanxin Zhang, and Cheng Wang. "A FAST METHOD FOR MEASURING THE SIMILARITY BETWEEN 3D MODEL AND 3D POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 6, 2016): 725–28. http://dx.doi.org/10.5194/isprsarchives-xli-b1-725-2016.
Full textAbstract:
This paper proposes a fast method for measuring the partial Similarity between 3D Model and 3D point Cloud (SimMC). It is crucial to measure SimMC for many point cloud-related applications such as 3D object retrieval and inverse procedural modelling. In our proposed method, the surface area of model and the Distance from Model to point Cloud (DistMC) are exploited as measurements to calculate SimMC. Here, DistMC is defined as the weighted distance of the distances between points sampled from model and point cloud. Similarly, Distance from point Cloud to Model (DistCM) is defined as the average distance of the distances between points in point cloud and model. In order to reduce huge computational burdens brought by calculation of DistCM in some traditional methods, we define SimMC as the ratio of weighted surface area of model to DistMC. Compared to those traditional SimMC measuring methods that are only able to measure global similarity, our method is capable of measuring partial similarity by employing distance-weighted strategy. Moreover, our method is able to be faster than other partial similarity assessment methods. We demonstrate the superiority of our method both on synthetic data and laser scanning data.
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Shi, Fangzhe, Jingxin Yang, Qiuyi Li, Junjie He, and Boning Chen. "3D Laser Scanning Acquisition and Modeling of Tunnel Engineering Point Cloud Data." Journal of Physics: Conference Series 2425, no. 1 (February 1, 2023): 012064. http://dx.doi.org/10.1088/1742-6596/2425/1/012064.
Full textAbstract:
Abstract For tunnel deformation analysis using traditional measurement methods to obtain tunnel section data, there are problems such as small data coverage and low efficiency. 3D laser scanning technology has the advantages of automatic, high precision and high efficiency in collecting the point cloud data of the target object, and can completely and accurately express the target entity. Based on the tunnel point cloud acquired by 3D laser scanner, the tunnel engineering modeling research is carried out in this paper. Firstly, the engineering survey and 3D reconstruction technology of shield tunnel were carried out based on 3D laser scanning technology. The total station layout control was used to scan the subway platform and tunnel interval, and the high-precision laser point cloud data were obtained. Secondly, a random sampling consistency algorithm is proposed to extract engineering measurement results such as tunnel axis and cross section. Finally, the 3D modeling of the tunnel is established by using the stretching setting out modeling method. Taking Yuzhu Tunnel planning and acceptance project as an example, the experimental results show that the method can effectively visualize the overall deformation of the tunnel, and provide accurate and scientific spatial data for tunnel engineering measurement, operation and maintenance.