Building edge points, as essential geometric features, are crucial for advancing smart city initiatives and ensuring the precise reconstruction of 3-D structures. However, existing methods struggle to effectively design point-to-edge distance constraints for accurate building edge point identification. In this letter, we propose a novel self-supervised learning (SSL)-based pretraining framework that integrates an innovative edge point identification loss function for extracting building edge points. Specifically, we use an SSL-based feature extractor, leveraging a masked autoencoder to generate pointwise features from the input building point clouds. These features are subsequently processed by the proposed edge point identification module, which optimizes three key distance-based loss functions: the distance between any input point and its nearest edge, the distance between candidate edge points and the projection of the input point, and the distance between candidate edge points and the edges themselves. The proposed framework demonstrates superior performance in edge point extraction across both partial and complete datasets, outperforming existing methods in edge point identification.

Currently, the complex features of remote sensing images complicate shadow detection, such as blurred shadow edges, a large number of fragmented shadows, and varying shadow colors. To address these challenges, a distraction-aware edge enhancement for shadow detection network (DESDNet) is proposed. It utilizes distraction-aware shadow (DS) modules to reduce false positives (FPs) and false negatives (FNs), while employing edge enhancement modules to improve shadow edge detection. In this network, the shallowest convolutional layers capture detailed shadow edge features, including many nonshadow background details, while the deepest convolutional layers, with larger receptive fields, effectively suppressing nonshadow pixels. The edge module integrates the two features to mitigates issues, such as edge blurring, fragmentation, and color inconsistency in shadows. The proposed network has been validated for feasibility on remote sensing image datasets, on which our experiments achieve the accuracies of 98.48% on the ISTD dataset and 96.70% on the SBU dataset, outperforming typical networks recently. The code is available fromhttps://github.com/sfs0/DESDNet/tree/main.

The LiDAR scanning approach captures a large amount of tree point cloud information, including the accurate coordinate of points, local topology, and overall geometry. However, the complex tree structures, e.g., curvature and occlusion of branches, bring challenges to the 3-D tree reconstruction. In this letter, we propose an innovative solution for obtaining the complete skeletons of individual trees and 3-D structures for modeling using point clouds. First, we obtain individual trees from input street scene and segment tree into small successive pieces, with the centers of each piece serving as skeleton candidate points. Second, we conduct the interpolation based on the Euclidean distance and orientation yields the entire skeleton completely. Finally, a high-precision 3-D model of trees is constructed by cylindrically fitting the skeleton relying on the optimized circular truncated cones depending on the branch orientation and cylindrical curvature. Experiments on various trees demonstrate the high efficiency and effectiveness of our method. Our method achieves 98% accuracy and takes less than 1 min in the reconstruction. Compared with other methods, our method reduces the time by more than 95%.

Low-light remote sensing image enhancement (LLIE) for unmanned aerial vehicles (UAVs) has significant scientific and practical value because unfavorable lighting conditions make capture more difficult, resulting in undesired images. As acquiring real-world low-light/normal-light image pairs in the field of remote sensing is almost infeasible, performing LLIE in an unpaired manner is practical and valuable. However, without the paired data as the supervision, learning an LLIE network is challenging. To address the challenges, the paper proposes a novel yet effective method to unpaired LLIE, which maximizes the mutual information between low-light and restored images through self-similarity contrastive learning (SSCL) in a fully unsupervised fashion within a single deep GAN framework, named CLEGAN. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself. The non-local patch sampling strategy in SSCL naturally makes the negative samples differ from the positive samples for discriminative representation. Moreover, the single GAN embeds the dual illumination perception module (DIPM) to handle the internal recurrence of information and overall uneven illumination distribution in remote sensing images. DIPM mainly consists of two cooperative blocks: spatial adaptive light adjustment module (SALAM) and global adaptive light adjustment module (GALAM). Specifically, SALAM exploits the internal recurrence of information in remote sensing images to encode a wider range of contextual information into local features and make proper light estimation. Simultaneously, GALAM enhances the most valuable illumination-related channels in the feature map to achieve better light estimation. The experiments on several datasets including low-light remote sensing image dataset and public low-light image datasets show that CLEGAN performs favorably against the existing unpaired LLIE approaches, and even outperforms several fully-supervised methods.

As many LiDAR point cloud processing steps, such as reconstruction, are often time- and memory-consuming, dividing LiDAR point clouds into subregions is common and necessary during preprocessing. However, the existing data dividing methods rely on tedious manual work or regular grids and result in oversegmentation around cutting lines. In this letter, we propose a new gap-based data dividing method for various LiDAR point clouds that can minimize the intersections between cutting lines and objects. The basic idea is to find a set of optimal paths that consist of gaps between objects as potential cutting lines. The experiments and comparisons in three data sets demonstrate that the proposed method is much better than the baseline method in terms visual inspection and cutting line quality.

The 3-D point cloud segmentation has played an important role in spatial structure analysis. Nowadays, segmentation methods either use a primitive-based strategy to fit points in predefined geometric shapes or group points based on their attributes (e.g., spatial distance). However, the required segmentation results, e.g., primitive level or object level, depend on the application. Therefore, this letter develops a semiautomatic method to extract contours for the users’ desired segmentation. First, we initialize a 3-D closed curve for the target. Second, we calculate the internal and external force based on the proposed vector flow to deform the curve. The deformation equation is solved based on the Euler equation and calculated iteratively. Finally, the curve is converged as object contours. After one removes contours, those disjoint points are grouped as the users’ desired instances. Experiments are conducted on various point clouds to demonstrate the effectiveness in terms of accuracy and consistency. Our quantitative evaluation outperformed selected primitive- and object-based methods, which presents a new viewpoint to the point cloud processing.

Nowadays, the classification of point clouds has become a fundamental problem in 3-D information study. Different from the deep learning process of natural images, 3-D point clouds are massive and unorganized, which can be difficultly captured features by the convolution process directly. This letter proposes a new augmentation convolutional neural network (ACNN) to classify point clouds by adding a key augmentation layer before the classical sampling and convolution structure. Input data will be augmented before each sampling layer, which brings abundant learning information to help the network capture more local structures. In order to make the augmentation more effective, we formulate the parameters of augmentation layers learnable in the learning process according to the loss function. The proposed augmentation is based on automatically tuning the magnitude of the smoothness, which plays a significant role in point cloud processing and provides local features, for example, edges, contours, and edges. Results show that we have achieved the overall accuracy of 92.52% and 89.11% in the object classification on ModelNet10 and ModelNet40, respectively, which shows our superiority over other methods. Besides, the ACNN achieves an average miscalculation error of 0.28 and cross-entropy loss of 0.48 in the classification of laser scanning point clouds, which shows high robustness to noise and density in the outdoor scene classification.

Building region extraction from ALS point clouds has been widely studied, whereas instance-level building mapping has been overlooked and remains unsolved. In this study, we present a method to extract individual buildings from ALS point clouds with the help of widely accessible polygonal footprints. The key idea is to merge roof segments to a set of building candidates, from which correct instances are selected by finding optimal matches between polygonal footprints and building candidates. The method has three steps: roof segmentation, building candidate generation, and instance-polygon matching. The method is tested on two large-scale scenes of different building types and can generally achieve high instance-level building mapping accuracy (around 90%) when there are large positioning errors (6.0 m) among polygons. Future work will focus on classification errors in preprocessing, shape inconsistency between point clouds and polygons, and building footprint delineation and updating in postprocessing.

One key challenge in the point cloud segmentation is the detection and split of overlapping regions between different planes. The existing methods depend on the similarity and the dissimilarity in neighbor regions without a global constraint, which brings the 'over-' and 'under-' segmentation in the results. Hence, this paper presents a pipeline of the accurate plane segmentation for point clouds to address the shortcoming in the local optimization. There are two phases included in the proposed segmentation process. One is a local phase to calculate connectivity scores between different planes based on local variations of surface normals. In this phase, a new optimal-vector-field is formulated to detect the plane intersections. The optimal-vector-field is large in magnitude at plane intersections and vanishing at other regions. The other one is a global phase to smooth local segmentation cues to mimic leading eigenvector computation in the graph-cut. Evaluation of two datasets shows that the achieved precision and recall is 94.50 percent and 90.81 percent on the collected mobile LiDAR data and obtains an average accuracy of 75.4 percent on an open benchmark, which outperforms the state-of-the-art methods in terms of completeness and correctness.

This paper aims to propose a new optimal hierarchical clustering approach to 3D mobile light detection and ranging (LiDAR) point clouds. The hierarchical clustering is performed on unorganized point clouds based on a proximity matrix that consists of a distance term and a direction term. In the dissimilarity calculation of two clusters, a pair of points from each of two clusters is selected, respectively, and Euclidean distances between the points are employed to define the distance term. The direction term is obtained by the differences of normal vectors at chosen points. The main contribution is that the cluster combination in the hierarchical clustering is optimized by a point-based graph model. The cluster combination is formulated as a problem of matching, optimized by finding the minimum-cost perfect matching in a bipartite graph. The results show that the proposed hierarchical clustering method succeeds in segmenting object from point clouds without any human-computer interaction and outperforms the state-of-the-art segmentation approaches in terms of completeness and correctness.

Automatic extraction of road curbs from uneven, unorganized, noisy, and massive 3-D point clouds is a challenging task. Existing methods often project 3-D point clouds onto 2-D planes to extract curbs. However, the projection causes loss of 3-D information, which degrades the performance of the detection. This paper presents a robust, accurate, and efficient method to extract road curbs from 3-D mobile LiDAR point clouds. Our method consists of two steps: 1) extracting candidate points of curbs based on the proposed novel energy function and 2) refining candidate points using the proposed least cost path model. We evaluated the method on a large scale of residential area (16.7 GB, 300 million points) and an urban area (1.07 GB, 20 million points) mobile LiDAR point clouds. Results indicate that the proposed method is superior to the state-of-the-art methods in terms of robustness, accuracy, and efficiency. The proposed curb extraction method achieved a completeness of 78.62% and a correctness of 83.29%. Experiments demonstrate that our method is a promising solution to extract road curbs from mobile LiDAR point clouds.

In this paper, a novel segmentation and recognition approach to automatically extract street lighting poles from mobile LiDAR data is proposed. First, points on or around the ground are extracted and removed through a piecewise elevation histogram segmentation method. Then, a new graph-cut-based segmentation method is introduced to extract the street lighting poles from each cluster obtained through a Euclidean distance clustering algorithm. In addition to the spatial information, the street lighting pole's shape and the point's intensity information are also considered to formulate the energy function. Finally, a Gaussian-mixture-model-based method is introduced to recognize the street lighting poles from the candidate clusters. The proposed approach is tested on several point clouds collected by different mobile LiDAR systems. Experimental results show that the proposed method is robust to noises and achieves an overall performance of 90% in terms of true positive rate.