Automatic 3D Building Detection and Modeling from Airborne LiDAR Point Clouds

84 Pages·2013·5.13 MB·English

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
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Airborne LiDAR
(Light Detection And Ranging)
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R.I.T Campus
Downtown Rochester
Alcoa Industrial Plant
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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
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Workflow
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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 .

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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
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Scene Classification
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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
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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
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