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3-D digital city vividly presents a real-world city and has been widely needed for many application domains. Numerous pole-like objects (PLOs), including trees, street lamps, and traffic signs, are an indispensable part of 3-D digital city. The point cloud data of mobile laser scanning (MLS) systems can capture both the geometric shape and geospatial coordinates of the PLOs while moving along the roads. This article is motivated to accurately extract and efficiently model PLOs from the point cloud data. The main contributions of this article are as follows: 1) a divergence-incorporated clustering algorithm is proposed to extract trunks accurately from the pole-like 3-D distribution perspective of point cloud; 2) an adaptive growing strategy of alternately extending and updating 3-D neighbors is proposed to get the complete canopy points of various shapes and density; and 3) the part-based modeling is proposed to synthesize the point cloud of PLOs with meaningful 3-D shapes, providing a way to model objects for the 3-D digital city vividly and efficiently. The proposed method is tested on three data sets with different interference, shape of the canopy, and point density. Experimental results demonstrate that the proposed method can extract and model the PLOs effectively and efficiently for 3-D digital city. The precision of trunk extraction is 98.45%, 98.08%, and 92.39%, the completeness of canopy extraction is 80.54%, 89.84%, and 89.29%, and the modeling time for a PLO is 0.011, 0.038, and 0.063 s in three data sets. [ABSTRACT FROM AUTHOR]

With the aid of global position system (GPS), mobile laser scanning (MLS) is able to provide 3-D geo-referenced point cloud that has centimeter-level accuracy. The MLS accuracy, however, degrades significantly due to the trajectory errors of the laser scanner and the residual systematic errors from the geo-referencing transformation process in the GPS-free environments. To solve this problem, this article presents a novel integration algorithm based on the weighted total least squares (WTLS) and the 3-D conformal coordinate transformation (3DCCT). In this new method, the 3-D point measurement model and the error propagation parameter vector in the MLS can be updated in real-time, and they can also adjust the geo-referenced coordinate transformation parameters and eliminate the influences of the residual systematic errors during MLS. In this article, the MLS mathematical model is first established, followed up by a detailed analysis for MLS error budget interpreting the effects of the individual error sources. Second, WTLS is used to correct the 3-D point measurement model of MLS and the error of propagation parameter vector; 3DCCT, WTLS, and ground control target feature constraints are applied to eliminate the residual systematic errors in the geo-referencing transformation process. Finally, several data sets from outdoor scenarios are used to evaluate and validate the proposed method. The experimental results demonstrate that the proposed method can significantly improve the overall accuracy of the MLS system. [ABSTRACT FROM AUTHOR]

This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: $x-z$ and $y-z$. The $z$ (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted $z$ values, along with the corresponding $x$ (or $y$) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for $z$ values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy. [ABSTRACT FROM PUBLISHER]

The assessment of pavement cracks is one of the essential tasks for road maintenance. This paper presents a novel framework, called ITVCrack, for automated crack extraction based on iterative tensor voting (ITV), from high-density point clouds collected by a mobile laser scanning system. The proposed ITVCrack comprises the following: 1) the preprocessing involving the separation of road points from nonroad points using vehicle trajectory data; 2) the generation of the georeferenced feature (GRF) image from the road points; and 3) the ITV-based crack extraction from the noisy GRF image, followed by an accurate delineation of the curvilinear cracks. Qualitatively, the method is applicable for pavement cracks with low contrast, low signal-to-noise ratio, and bad continuity. Besides the application to GRF images, the proposed framework demonstrates much better crack extraction performance when quantitatively compared to existing methods on synthetic data and pavement images. [ABSTRACT FROM PUBLISHER]

Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future. [ABSTRACT FROM PUBLISHER]

This paper presents an automated algorithm for rapidly and effectively detecting cars directly from large-volume 3-D point clouds. Rather than using low-order descriptors, a multilayer feature generation model is created to obtain high-order feature representations for 3-D local patches through deep learning techniques. To handle cars with different levels of incompleteness caused by data acquisition ways and occlusions, a hierarchical visibility estimation model is developed to augment Hough voting. Considering scale and orientation variations in the azimuth direction, a set of multiscale Hough forests is constructed to rotationally cast votes to estimate cars' centroids. Quantitative assessments show that the proposed algorithm achieves average completeness, correctness, quality, and F1-measure of 0.94, 0.96, 0.90, and 0.95, respectively, in detecting 3-D cars. Comparative studies also demonstrate that the proposed algorithm outperforms the other four existing algorithms in accurately and completely detecting 3-D cars from large-scale 3-D point clouds. [ABSTRACT FROM AUTHOR]

The maintenance of subway tunnels is vital to ensure the safety of their daily operation. Issues experienced by shield subway tunnels, especially the water leakages, require rapid and accurate detection and diagnosis. Due to the large number of disturbances in the tunnels, conventional algorithms face limitations when extracting discriminative features. To solve this problem, we propose a novel and efficient deep learning model for extracting multiscale and discriminative features of water leakages based on mobile laser scanning (MLS) point cloud intensity images. A new residual network module (Res2Net) is integrated with a cascade structure to form a unified model to extract the multiscale features of water leakages. The model can fully consider geometric characteristics of water leakages and grade the residual connections in a single residual block. This expands the size of receptive field in each network layer and can better facilitate the extraction of geometric characteristics of water leakages. Finally, we verify the advantages of the proposed method via experiments on five water leakage datasets of tunnel intensity images converted from point clouds obtained by a self-developed MLS system and compare its performance with other methods. [ABSTRACT FROM AUTHOR]

Accurate and efficient extraction of road marking plays an important role in road transportation engineering, automotive vision, and automatic driving. In this article, we proposed a dense feature pyramid network (DFPN)-based deep learning model, by considering the particularity and complexity of road marking. The DFPN concatenated its shallow feature channels with deep feature channels so that the shallow feature maps with high resolution and abundant image details can utilize the deep features. Thus, the DFPN can learn hierarchical deep detailed features. The designed deep learning model was trained end to end for road marking instance extraction with mobile laser scanning (MLS) point clouds. Then, we introduced the focal loss function into the optimization of deep learning model in road marking segmentation part, to pay more attention to the hard-classified samples with a large extent of background. In the experiments, our method can achieve better results than state-of-the-art methods on instance segmentation of road markings, which illustrated the advantage of the proposed method. [ABSTRACT FROM AUTHOR]

The 3-D information collected from sample plots is significant for forest inventories. Terrestrial laser scanning (TLS) has been demonstrated to be an effective device in data acquisition of forest plots. Although TLS is able to achieve precise measurements, multiple scans are usually necessary to collect more detailed data, which generally requires more time in scan preparation and field data acquisition. In contrast, mobile laser scanning (MLS) is being increasingly utilized in mapping due to its mobility. However, the geometrical peculiarity of forests introduces challenges. In this article, a test backpack-based MLS system, i.e., backpack laser scanning (BLS), is designed for forest plot mapping without a global navigation satellite system/inertial measurement unit (GNSS-IMU) system. To achieve accurate matching, this article proposes to combine the line and point features for calculating transformation, in which the line feature is derived from trunk skeletons. Then, a scan-to-map matching strategy is proposed for correcting positional drift. Finally, this article evaluates the effectiveness and the mapping accuracy of the proposed method in forest sample plots. The experimental results indicate that the proposed method achieves accurate forest plot mapping using the BLS; meanwhile, compared to the existing methods, the proposed method utilizes the geometric attributes of the trees and reaches a lower mapping error, in which the mean errors and the root square mean errors for the horizontal/vertical direction in plots are less than 3 cm. [ABSTRACT FROM AUTHOR]

The automatic classification of 3-D point clouds is publicly known as a challenging task in a complex road environment. Specifically, each point is automatically classified into a unique category label, and then, the labels are used as clues for semantic analysis and scene recognition. Instead of heuristically extracting handcrafted features in traditional methods to classify all points, we put forward an end-to-end octree-based fully convolutional network (FCN) to classify 3-D point clouds in an urban road environment. There are four contributions in this paper. The first is that the integration and comprehensive uses of OctNet and FCN greatly decrease the computing time and memory demands compared with a dense 3-D convolutional neural network (CNN). The second is that the octree-based network is strengthened by means of modifying the cross-entropy loss function to solve the problems of an unbalanced category distribution. The third is that an Inception-ResNet block is united with our network, which enables our 3-D CNN to effectively learn how to classify scenes containing objects at multiple scales and improve classification accuracy. The last is that an open source data set (HuangshiRoad data set) with ten different classes is introduced for 3-D point cloud classification. Three representative data sets [Semantic3D, WHU_MLS (blocks I and II), and HuangshiRoad] with different covered areas and numbers of points and classes are selected to evaluate our proposed method. The experimental results show that the overall classification accuracy is appreciable, with 89.4% for Semantic3D, 82.9% for WHU_MLS block I, 91.4% for WHU_MLS block II, and 94% for HuangshiRoad. Our deep learning approach can efficiently classify 3-D dense point clouds in an urban road environment measured by a mobile laser scanning (MLS) system or static LiDAR. [ABSTRACT FROM AUTHOR]

We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data. [ABSTRACT FROM AUTHOR]

Quantifying near-field displacements can help enable a better understanding of earthquake physics and hazards. To date, established remote sensing techniques have failed to recover subcentimeter-level near-field displacements at the scale and resolution required for shallow fault physical investigations. In this paper, methods are developed to rapidly extract planar parameters, using fast parallel approaches and an alternative registration approach is employed to automatically match the planes extracted from pairwise temporally spaced mobile laser scanning (MLS) and Airborne laser scanning (ALS) data sets along the Napa fault. The features extracted from two temporally spaced point clouds are then used to calculate rigid-body transformation parameters. The production of robust and accurate deformation maps requires the selection of appropriate planar feature extraction and feature mapping criteria. Rigorously propagated point accuracy estimates are employed to produce realistic estimated errors for the transformation parameters. Displacements of each aggregate study area are computed separately from left and right sides of the fault and compared to be within 3 mm of alinement array displacements. Local differential displacements show distinct patterns which, compared to alinement array measurements, were found to agree within the confidence bounds. The findings demonstrate the ability to accurately estimate near-field deformations from repeated MLS or ALS scans of earthquake-prone urban areas. ALS is also used in conjunction with the MLS data sets, illustrating the algorithm’s ability to accommodate different LiDAR collection modalities at subcentimeter-level accuracy. The automated planar extraction and registration is an important contribution to the study of near-field earthquake dynamics and can be used as input observations for future geological inversion models. [ABSTRACT FROM AUTHOR]