A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Point cloud generation for critical transportation infrastructure through Bézier curve
https://orcid.org/0000-0002-3536-9348
Abstract While Light Detection and Ranging (LiDAR)-based sensors exhibit considerable potential for transportation infrastructure management, not all infrastructure elements can be comprehensively captured by point clouds, leading to the formation of undesirable “holes” due to both temporary and permanent occlusions. It is imperative to devise mechanisms for identifying and predicting the missing data within these “holes” to ensure the continuous acquisition of critical inventory information. This paper describes a method for generating point clouds based on Bézier curves, which effectively fills the voids within the infrastructure. This method comprises three integral processes, including angle-based boundary detection, identification of principal elevation change, and Bézier curve-based hole filling. The method demonstrates promising results on different roadside surfaces and at different ranges of scales of “holes”. Case studies on the sidewalk, and sound barrier inventories show that the proposed method can significantly improve the quality of the point cloud data for subsequent measurements.
Highlights Develops an automated Bézier curve-based data generation methodology for LiDAR point cloud data from common transportation scenes. Develops boundary detection and principal elevation change identification methods for synthesizing missing data’s characteristics. Develops comprehensive validation and case studies to quantitatively demonstrate the performance of the developed method. Advances the understanding of LiDAR point cloud data and maximizes its utilization in critical transportation asset management applications.
Point cloud generation for critical transportation infrastructure through Bézier curve
https://orcid.org/0000-0002-3536-9348
Abstract While Light Detection and Ranging (LiDAR)-based sensors exhibit considerable potential for transportation infrastructure management, not all infrastructure elements can be comprehensively captured by point clouds, leading to the formation of undesirable “holes” due to both temporary and permanent occlusions. It is imperative to devise mechanisms for identifying and predicting the missing data within these “holes” to ensure the continuous acquisition of critical inventory information. This paper describes a method for generating point clouds based on Bézier curves, which effectively fills the voids within the infrastructure. This method comprises three integral processes, including angle-based boundary detection, identification of principal elevation change, and Bézier curve-based hole filling. The method demonstrates promising results on different roadside surfaces and at different ranges of scales of “holes”. Case studies on the sidewalk, and sound barrier inventories show that the proposed method can significantly improve the quality of the point cloud data for subsequent measurements.
Highlights Develops an automated Bézier curve-based data generation methodology for LiDAR point cloud data from common transportation scenes. Develops boundary detection and principal elevation change identification methods for synthesizing missing data’s characteristics. Develops comprehensive validation and case studies to quantitatively demonstrate the performance of the developed method. Advances the understanding of LiDAR point cloud data and maximizes its utilization in critical transportation asset management applications.
Point cloud generation for critical transportation infrastructure through Bézier curve
https://orcid.org/0000-0002-3536-9348
Abstract While Light Detection and Ranging (LiDAR)-based sensors exhibit considerable potential for transportation infrastructure management, not all infrastructure elements can be comprehensively captured by point clouds, leading to the formation of undesirable “holes” due to both temporary and permanent occlusions. It is imperative to devise mechanisms for identifying and predicting the missing data within these “holes” to ensure the continuous acquisition of critical inventory information. This paper describes a method for generating point clouds based on Bézier curves, which effectively fills the voids within the infrastructure. This method comprises three integral processes, including angle-based boundary detection, identification of principal elevation change, and Bézier curve-based hole filling. The method demonstrates promising results on different roadside surfaces and at different ranges of scales of “holes”. Case studies on the sidewalk, and sound barrier inventories show that the proposed method can significantly improve the quality of the point cloud data for subsequent measurements.
Highlights Develops an automated Bézier curve-based data generation methodology for LiDAR point cloud data from common transportation scenes. Develops boundary detection and principal elevation change identification methods for synthesizing missing data’s characteristics. Develops comprehensive validation and case studies to quantitatively demonstrate the performance of the developed method. Advances the understanding of LiDAR point cloud data and maximizes its utilization in critical transportation asset management applications.
Point cloud generation for critical transportation infrastructure through Bézier curve
Hou, Qing (author) / Ai, Chengbo (author)
2023-10-30
Article (Journal)
Electronic Resource
English
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