共查询到20条相似文献,搜索用时 15 毫秒
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《Advanced Engineering Informatics》2015,29(4):930-939
This paper addresses the problem of automated registration of multi-view point clouds generated by a 3D scanner using sphere targets. First, sphere targets are detected from each point cloud. The centroids of the detected targets in each point cloud are then used for rough registration. Congruent triangles are computed from the centroids for the correspondence among them, with which a rigid body transformation is obtained to bring the two point clouds together as closely as possible. After the initial registration, the two point clouds are further registered by refining the position and orientation of the point clouds using the underlying geometric shapes of the targets. These registration steps are integrated into one system that allows two input point clouds automatically registered with no user intervention. Real examples are used to demonstrate the performance of the point cloud registration. 相似文献
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Yu Liu Author Vitae 《Computer aided design》2008,40(5):576-594
A nonparametric clustering algorithm, called cell mean shift (CMS), is developed to extract clusters of a set of points on the Gaussian sphere . It is computationally more efficient than the traditional mean shift (MS). Based on the singular value decomposition, the dimensional analysis is introduced to classify these clusters into point-, curve-, and area-form clusters. Each cluster is the Gaussian image of a set of points which will be examined by a connected search in . An orientation analysis of the Gaussian map to area-form clusters is applied to identify hyperbolic and elliptical regions. A signed point-to-plane distance function is used to identify points of convex and concave regions. Segmentation results of several real as well as synthetic point clouds, together with complexity analyses, are presented. 相似文献
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This article presents a new approach to segmenting building rooftops from airborne lidar point clouds. A progressive morphological filter technique is first applied for separation between ground and non-ground points. For the non-ground points, a region-growing algorithm based on a plane-fitting technique is used to separate building points from vegetation points. Then, an adaptive Random Sample Consensus (RANSAC) algorithm based on a grid structure is developed to improve the probability of selecting an uncontained sample from the localized sampling. The distance, standard deviation and normal vector are integrated to keep topological consistency among building rooftop patches during building rooftop segmentation. Finally, the remaining points are mapped on to the extracted planes by a post-processing technique to improve the segmentation accuracy. The results for buildings with different roof complexities are presented and evaluated. 相似文献
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ABSTRACTWe propose a comprehensive strategy to reconstruct urban building geometry from three-dimensional (3D) point clouds. First, the point clouds are segmented using a rough-detail segmentation algorithm, and refinements guided by topological relationships are performed to rectify the segmentation mistakes. Then, the semantic features (such as facades and windows) that belong to the buildings are recognized and extracted. Next, each facade is cut into a sequence of slices. The initial models are recovered by sequentially detecting and connecting the anchor points. Finally, due to the regular arrangements of windows, a template-matching method relying on the similarity and repetitiveness of the windows is proposed to recover the details on building facades. The experimental results demonstrate that our method can automatically reconstruct the building geometry and detailed window structures are better depicted. 相似文献
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We use a moving parabolic approximation (MPA) to reconstruct a triangular mesh that approximates the underlying surface of a point cloud from closed objects. First, an efficient strategy is presented for constructing a hierarchical grid with adaptive resolution and generating an initial mesh from point clouds. By implementing the MPA algorithm, we can estimate the differential quantities of the underlying surface, and subsequently, we can obtain the local quadratic approximants of the squared distance function for any point in the vicinity of the target shape. Thus, second, we adapt the mesh to the target shape by an optimization procedure that minimizes a quadratic function at each step. With the objective of determining the geometrical features of the target surface, we refine the approximating mesh selectively for the non-flat regions by comparing the estimated curvature from the point clouds and the estimated curvatures computed from the current mesh. Finally, we present various examples that demonstrate the robustness of our method and show that the resulting reconstructions preserve geometric details. 相似文献
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Rico Richter Markus Behrens Jürgen Döllner 《International journal of remote sensing》2013,34(23):8408-8424
A large number of remote-sensing techniques and image-based photogrammetric approaches allow an efficient generation of massive 3D point clouds of our physical environment. The efficient processing, analysis, exploration, and visualization of massive 3D point clouds constitute challenging tasks for applications, systems, and workflows in disciplines such as urban planning, environmental monitoring, disaster management, and homeland security. We present an approach to segment massive 3D point clouds according to object classes of virtual urban environments including terrain, building, vegetation, water, and infrastructure. The classification relies on analysing the point cloud topology; it does not require per-point attributes or representative training data. The approach is based on an iterative multi-pass processing scheme, where each pass focuses on different topological features and considers already detected object classes from previous passes. To cope with the massive amount of data, out-of-core spatial data structures and graphics processing unit (GPU)-accelerated algorithms are utilized. Classification results are discussed based on a massive 3D point cloud with almost 5 billion points of a city. The results indicate that object-class-enriched 3D point clouds can substantially improve analysis algorithms and applications as well as enhance visualization techniques. 相似文献
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Shijun Tang Pinliang Dong Bill P. Buckles 《International journal of remote sensing》2013,34(4):1373-1385
In this article, a novel method is proposed for three-dimensional (3D) canopy surface reconstruction of trees using a region-based level set method. Both individual tree crowns and clusters of trees are first marked for further exploration. Multiple horizontal slices corresponding to different heights are obtained. The 3D structure of tree canopy is built using raw data from lidar point clouds. Also, new applications are proposed based on the new method for 3D forest reconstruction. The biomass parameters of the forest, including tree intersection area, tree equivalent crown radius, and canopy volume, can be calculated from stacking 2D slices of trees. Tree types are also identified and classified. The results indicate that this approach is effective for 3D surface reconstruction of forests including individual trees and clusters of trees, and that critical forest parameters (such as tree intersection area, tree position, and canopy volume) can be derived for the evaluation and measurement of biophysical parameters of forests. 相似文献
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Andreas Jochem Bernhard Höfle Volker Wichmann Martin Rutzinger Alexander Zipf 《Computers, Environment and Urban Systems》2012
Most algorithms performing segmentation of 3D point cloud data acquired by, e.g. Airborne Laser Scanning (ALS) systems are not suitable for large study areas because the huge amount of point cloud data cannot be processed in the computer’s main memory. In this study a new workflow for seamless automated roof plane detection from ALS data is presented and applied to a large study area. The design of the workflow allows area-wide segmentation of roof planes on common computer hardware but leaves the option open to be combined with distributed computing (e.g. cluster and grid environments). The workflow that is fully implemented in a Geographical Information System (GIS) uses the geometrical information of the 3D point cloud and involves four major steps: (i) The whole dataset is divided into several overlapping subareas, i.e. tiles. (ii) A raster based candidate region detection algorithm is performed for each tile that identifies potential areas containing buildings. (iii) The resulting building candidate regions of all tiles are merged and those areas overlapping one another from adjacent tiles are united to a single building area. (iv) Finally, three dimensional roof planes are extracted from the building candidate regions and each region is treated separately. The presented workflow reduces the data volume of the point cloud that has to be analyzed significantly and leads to the main advantage that seamless area-wide point cloud based segmentation can be performed without requiring a computationally intensive algorithm detecting and combining segments being part of several subareas (i.e. processing tiles). A reduction of 85% of the input data volume for point cloud segmentation in the presented study area could be achieved, which directly decreases computation time. 相似文献
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We present an automatic system to reconstruct 3D urban models for residential areas from aerial LiDAR scans. The key difference between downtown area modeling and residential area modeling is that the latter usually contains rich vegetation. Thus, we propose a robust classification algorithm that effectively classifies LiDAR points into trees, buildings, and ground. The classification algorithm adopts an energy minimization scheme based on the 2.5D characteristic of building structures: buildings are composed of opaque skyward roof surfaces and vertical walls, making the interior of building structures invisible to laser scans; in contrast, trees do not possess such characteristic and thus point samples can exist underneath tree crowns. Once the point cloud is successfully classified, our system reconstructs buildings and trees respectively, resulting in a hybrid model representing the 3D urban reality of residential areas. 相似文献
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对标准站立测量姿态下的人体表面点云数据的拓扑特征检测与自动分割进行了研究,提出基于全景深度图像表示的人体点云表面拓扑特征检测和自动分割新方法。首先把人体表面的点云数据转换为圆柱极坐标形式,获得人体扫描表面的全景深度图像表示,根据全景深度图像中的层次信息自动检测人体表面的拓扑特征,并根据拓扑特征把人体分割成5个功能结构。实验证明这种方法改进了人体表面点云数据的拓扑特征检测和自动分割的效率和精度。 相似文献
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Ono Taishi Kubo Hiroyuki Tanaka Kenichiro Funatomi Takuya Mukaigawa Yasuhiro 《计算可视媒体(英文)》2019,5(4):325-336
Computational Visual Media - In this paper, we present a practical method for reconstructing the bidirectional reflectance distribution function (BRDF) from multiple images of a real object... 相似文献
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Automatic non-rigid registration of multi-strip point clouds from mobile laser scanning systems 总被引:1,自引:0,他引:1
Mobile laser scanning (MLS) systems equipped with precise Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) positioning devices are being used at an increasing rate for production of the high-accurate driving maps because of its safety and high performance in collection of 3D spatial data. In practice, GNSS signals may be blocked out by trees or buildings etc., and the errors of IMU are accumulated over time, leading to misalignments ranging from decimetre level to sub-metre level between point clouds from back and forth scans, or among multiple excursions. In this article, we propose a new time-variant model and an automatic solution to align the multi-strip MLS point clouds. Our methods are divided into three key steps: preprocessing to get representative points, two-step Iterative Closest Point registration to obtain correspondences, and time-variant errors estimation and correction of point clouds. We verified the solution using test data scanned in city road and highway environment. The experimental results demonstrate that the precision of the point clouds is significantly improved and the root mean square errors are about 4–5 cm. 相似文献
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Pouria Babahajiani Lixin Fan Joni-Kristian Kämäräinen Moncef Gabbouj 《Machine Vision and Applications》2017,28(7):679-694
3D urban maps with semantic labels and metric information are not only essential for the next generation robots such autonomous vehicles and city drones, but also help to visualize and augment local environment in mobile user applications. The machine vision challenge is to generate accurate urban maps from existing data with minimal manual annotation. In this work, we propose a novel methodology that takes GPS registered LiDAR (Light Detection And Ranging) point clouds and street view images as inputs and creates semantic labels for the 3D points clouds using a hybrid of rule-based parsing and learning-based labelling that combine point cloud and photometric features. The rule-based parsing boosts segmentation of simple and large structures such as street surfaces and building facades that span almost 75% of the point cloud data. For more complex structures, such as cars, trees and pedestrians, we adopt boosted decision trees that exploit both structure (LiDAR) and photometric (street view) features. We provide qualitative examples of our methodology in 3D visualization where we construct parametric graphical models from labelled data and in 2D image segmentation where 3D labels are back projected to the street view images. In quantitative evaluation we report classification accuracy and computing times and compare results to competing methods with three popular databases: NAVTEQ True, Paris-Rue-Madame and TLS (terrestrial laser scanned) Velodyne. 相似文献
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3D object pose estimation for robotic grasping and manipulation is a crucial task in the manufacturing industry. In cluttered and occluded scenes, the 6D pose estimation of the low-textured or textureless industrial object is a challenging problem due to the lack of color information. Thus, point cloud that is hardly affected by the lighting conditions is gaining popularity as an alternative solution for pose estimation. This article proposes a deep learning-based pose estimation using point cloud as input, which consists of instance segmentation and instance point cloud pose estimation. The instance segmentation divides the scene point cloud into multiple instance point clouds, and each instance point cloud pose is accurately predicted by fusing the depth and normal feature maps. In order to reduce the time consumption of the dataset acquisition and annotation, a physically-simulated engine is constructed to generate the synthetic dataset. Finally, several experiments are conducted on the public, synthetic and real datasets to verify the effectiveness of the pose estimation network. The experimental results show that the point cloud based pose estimation network can effectively and robustly predict the poses of objects in cluttered and occluded scenes. 相似文献
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Agostinho de Medeiros Brito Junior Adri?o Duarte Dória Neto Jorge Dantas de Melo Luiz Marcos Garcia Goncalves 《Neural Networks, IEEE Transactions on》2008,19(6):1130-1140
In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness. 相似文献