Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds PDF
Preview Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds
Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds Shaohui Sun Advisor: Carl Salvaggio, Ph.D. Digital Imaging and Remote Sensing Lab Chester F. Carlson Center for Imaging Science Rochester Institute of Technology, Rochester, NY 1 Airborne LiDAR (Light Detection And Ranging) 2 R.I.T Campus Downtown Rochester Alcoa Industrial Plant 3 Objective • Input: unorganized airborne LiDAR points represented by Universal Transverse Mercator (UTM) coordinates (easting, northing, elevation) • Process: automatically detect and model man-made building structures with complex roof shapes involving no rooftop template • Output: polyhedral models or triangulated mesh models of buildings; hole-free terrain surface 4 Workflow 5 Related Works • Jinhui et al, Musialski et al, and Haala et al provided comprehensive reviews on 3D urban reconstruction. • Ground level data vs. Aerial data • Image-based reconstruction vs. LiDAR-based reconstruction • Fusion of image and LiDAR • Key component development vs. Workflow development • This work is aerial LiDAR-based workflow development . 6 Related Works (cont.) • Vegetation detection: Secord et al (image + LiDAR), Charaniya et al (supervised), Sun et al (elevation filter), Anguelov et al (MRF, ground level target segmentation), Sedlack et al (interactive graph cuts based) – Goal: Unsupervised, no spectral information involved • Building footprint detection: Maas et al (GIS info involved), Weidner et al (DSM thresholding), Wang et al (pre- classification needed), Lafarge et al (DEM from satellite images) – Goal: no additional info required, treat building footprints as clusters 7 Scene Classification 8 Vegetation Detection • Vegetation detection using shape analysis on high resolution Digital Surface Model (HDSM) – In 2D space, less sophisticated • Vegetation detection using the graph cuts scheme – In 3D space, more sophisticated, adopted by the upcoming process 9 Vegetation Detection • Vegetation detection using shape analysis on high resolution Digital Surface Model (HDSM) – In 2D space, less sophisticated • Vegetation detection using the graph cuts scheme – In 3D space, more sophisticated, adopted by the upcoming process 10
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