Dawei Zai

3D road surface extraction from mobile laser scanning point clouds

This paper presents a new algorithm to directly extract 3D road boundaries from mobile laser scanning (MLS) point clouds. The algorithm includes two stages: 1) non-ground point removal by a voxel-based elevation filter, and 2) 3D road surface extraction by curb-line detection based on energy minimization and graph cuts. The proposed algorithm was tested on a dataset acquired by a RIEGL VMX-450 MLS system. The results fully demonstrate the effectiveness and superiority of the proposed algorithm.

Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds

As one of the most common features in the man-made environments, straight lines play an important role in many applications. In this paper, we present a new framework to extract line segments from large-scale point clouds. The proposed method is fast to produce results, easy for implementation and understanding, and suitable for various point cloud data. The key idea is to segment the input point cloud into a collection of facets efficiently. These facets provide sufficient information for determining linear features in the local planar region and make line segment extraction become relatively convenient. Moreover, we introduce the concept “number of false alarms” into 3-D point cloud context to filter the false positive line segment detections. We test our approach on various types of point clouds acquired from different ways. We also compared the proposed method with several other methods and provide both quantitative and visual comparison results. The experimental results show that our algorithm is efficient and effective, and produce more accurate and complete line segments than the comparative methods. To further verify the accuracy of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs these line segments on point clouds registration.

Solar Potential Analysis Method Using Terrestrial Laser Scanning Point Clouds

Analysis of sunlight or solar potential requires the data for a targeted scene to be authentic and accessible as much as possible. In this regard, the data for a real-world scene acquired by ground-based laser scanners are comprehensive and convenient, thus potentially meeting this requirement. To get an accurate result and offer an automatic solution for sunlight analysis, this paper proposes a solar potential analysis method that is run directly on 3-D ground laser scanning point clouds. Our method simulates natural illumination, sunlight hours, and solar radiation of the targeted scene for a specified period. This method first extracts the region of interest (ROI) to obtain targeted points. Then, we compute solar position as a virtual light source and propose a control method acting on the ROI to constraint deviation from the point light source. Finally, we adopt the generalized hidden point removal algorithm to cast shadow of obstruction on the ROI. Besides, experiments to validate the shading method results are carried out for three different periods. The quantitative results in the Xiamen case evaluated by the Hausdorff distance demonstrate the advantage and feasibility of our proposed method.

Inventory of 3D street lighting poles using mobile laser scanning point clouds

This paper presents a novel approach for extracting street lighting poles directly from MLS point clouds. The approach includes four stages: 1) elevation filtering to remove ground points, 2) Euclidean distance clustering to cluster points, 3) voxel-based normalized cut (Ncut) segmentation to separate overlapping objects, and 4) statistical analysis of geometric properties to extract 3D street lighting poles. A Dataset acquired by a RIEGL VMX-450 MLS system are tested with the proposed approach. The results demonstrate the efficiency and reliability of the proposed approach to extract 3D street lighting poles.

3-D Road Boundary Extraction From Mobile Laser Scanning Data via Supervoxels and Graph Cuts

Effective extraction of road boundaries plays a significant role in intelligent transportation applications, including autonomous driving, vehicle navigation, and mapping. This paper presents a new method to automatically extract 3-D road boundaries from mobile laser scanning (MLS) data. The proposed method includes two main stages: supervoxel generation and 3-D road boundary extraction. Supervoxels are generated by selecting smooth points as seeds and assigning points into facets centered on these seeds using several attributes (e.g., geometric, intensity, and spatial distance). 3-D road boundaries are then extracted using the

Earthwork volumes estimation in asphalt pavement reconstruction using a mobile laser scanning systerm

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An efficiently volumetric fusing method for structure-frome-motion and terrestrial point cloud

-shape algorithm and the graph cuts-based energy minimization algorithm. The proposed method was tested on two data sets acquired by a RIEGL VMX-450 MLS system. Experimental results show that road boundaries can be robustly extracted with an average completeness over 95%, an average correctness over 98%, and an average quality over 94% on two data sets. The effectiveness and superiority of the proposed method over the state-of-the-art methods is demonstrated.

Exploiting location information to detect light pole in mobile LiDAR point clouds

This paper presents a novel method for estimating earthwork volumes in asphalt pavement reconstruction using a mobile laser scanning (MLS) system. First, based on the static targets, this method registers two point cloud datasets into the same coordinate system, which respectively are acquired in the reconstructing road before and after asphalting. Next, road surface points are detected from each point cloud using a curb-based method, and further divided into a set of blocks. Afterwards, the blocks are perpendicularly partitioned into grids, where two surface features are extracted using the RANSAC. Finally, the volume of each grid is calculated according to these two surface features. The proposed algorithm has been tested on two sets of point clouds acquired by a RIEGL VMX-450 MLS system in the reconstructing road before and after asphalting. The results demonstrate the accuracy and efficiency of the proposed algorithm in estimating earthwork volumes.

Automated segmentation of LiDAR point clouds for building rooftop extraction

Airborne acquisition and ground-view 3D point cloud provide complementary 3D information at city scale. A complete but lacks ground-view details, while the latter is incomplete for higher floors and severe occlusion. In this paper, First, a volumetric fusion method based on graph cuts were applied for fusing of airborne and terrestrial 3D LiDAR data. Second, we propose a method of constraints based on the local centroid of point cloud to eliminate the gap of fusion boundary. Finally, the experiments show that the improved fusion algorithm implement blending effectively.

Rotation-and-scale-invariant airplane detection in high-resolution satellite images based on deep-Hough-forests

With rapid development of light detection and ranging (LiDAR) technologies, three dimensional point clouds increasingly become a new approach to sense the world. In our previous work, light poles were detected from mobile LiDAR point clouds without using their locations. In this paper, we improve our previous work by considering location information between two neighboring light poles to reduce false alarm. In the proposed method, the potential light poles are first detected by the extended Hough Forest Framework. Then, a gaussian distribution is exploited to model the distance between two light poles by using locations of those detected light poles. Finally, inaccurately detected light poles are removed by considering the distance between two adjacent objects. We evaluate our proposed method on mobile LiDAR point clouds acquired by RIEGL VMX-450 system. On the basis of the experimental test instances, we demonstrate improved accuracy on light pole detection.