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Lidar and Structure-from-Motion (SfM) technology have been widely used for collecting 3D point cloud data at high resolution and accuracy for various transportation applications, including infrastructure modeling and lane marking quality assessments.<\/span><\/p>\n Professors <\/span>Erzhuo Che<\/span><\/a> and <\/span>Michael Olsen<\/span><\/a> from Oregon State University (OSU) developed and implemented a framework named Vo-Norvana as part of a National Science Foundation-funded research project. This framework automates a significant portion of data processing by organizing the point cloud into individual segments\/surfaces (Figure 1). Despite the extensive use of point cloud data in transportation applications, its complexity and volume present challenges in effective utilization.<\/span><\/p>\n (a)<\/p><\/div>\n (b)<\/p><\/div>\n Figure 1. Example of Vo-Norvana point cloud segmentation: (a) raw mobile lidar point cloud data; (b) segmentation result where each color represents a segment.<\/em><\/p>\n Through the PacTrans Success Story Tech Transfer funding, the team worked to make the technology more accessible. They produced an intuitive graphical user interface (GUI) for Vo-Norvana, featuring functions such as point cloud file management, downsampling, and segmentation parameter adjustments. Additionally, the software was made compatible with both Windows and Linux environments.<\/span><\/p>\n![\"\"](\"http:\/\/depts.washington.edu\/pactrans\/wp-content\/uploads\/2024\/03\/PacTrans-Success-Story_Vonorvana_Che-Final_Page_2_Image_0001-300x172.jpg\"){kind=spacer}
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