SmartBoxes for Interactive Urban Reconstruction
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Transcript of SmartBoxes for Interactive Urban Reconstruction
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SmartBoxes for Interactive Urban Reconstruction
Liangliang Nan1, Andrei Sharf1, Hao Zhang2, Daniel Cohen-Or3, Baoquan Chen1
1 Shenzhen Institutes of Advanced Technology (SIAT), China2 Simon Fraser University, Canada
3University of Tel Aviv, Israel
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Virtual BerlinVirtual Philadelphia
3D Cities
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Acquisition of Urban Environments
Cameras/videos
Remote Sensing Systems
3D Digital CityAuto-mounted LIDAR
Airborne LIDAR
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3D LiDAR scanner
• Street-level• 60km/h• 180 pitch• [100-300m] range • 100K points/second• 5cm XY accuracy
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Outdoor Urban Scanning
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Imperfect Scans - Occlusions
• Point cloud contains holes due to various occlusions (“shadows”)
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Imperfect Scans – Angle & Range
• Oblique scanning angle• Laser energy attenuation
on range
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Urban Building Characteristics
• Repetitions, intra symmetry and regularity• Axis-aligned basic primitives• Dominant planes
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SmartBoxes
• Box-up and Smart!
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SmartBoxes
• Box prior shape fitting • Smart context awareness
Both
Context
Data
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Live Demo
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Related Work
• Procedural modeling of buildings and facades [Wonka2003;Muller2007]
• Automatic 3D reconstruction from 2D images [Zisserman2002;Xiao2009;Furukawa2009]
• Interactive modeling of architectural structures [Debevec1996;Schindler2003; Xiao2008;Sinha2008;Jiang2009]
• Primitive fitting to data [Gal2007;Schnabel 2009]
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Preprocessing
• Automatic detection of planes and edges assuming dominant orthogonal axes– RANSAC planes– Line sweep edges
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Snapping a Box
• 2D rubber band ROI• Collect planes, edges, corners• Find the best fitting box using
data fitting force D(B,P)
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Data fitting D(B,P) snap
Facet Edge
• Data fitting force
Data quality(Confidence + Density)
Distance
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Grouping
• Simple SmartBox Compound SmartBox• Align to remove gaps and intersections
– cluster and align close to co-linear edges
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Drag-and-drop context C(Bi-1, Bi)
• The context of Bi
••• Bi-1Bi-2Bi-3
Interval Alignment Scale
Context
Bi
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Drag-and-drop context C(Bi-1, Bi)
• The context of Bi
– Interval term
••• Bi-1Bi-2Bi-3Bi
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Drag-and-drop context C(Bi-1, Bi)
• The context of Bi
– Alignment term
••• BiBi-1Bi-2Bi-3
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Bi
Drag-and-drop context C(Bi-1, Bi)
• The context of Bi
– Scale term
••• BiBi-1Bi-2Bi-3
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Discrete objective minimization
• Find linear transformation T(excluding rotation) to minimize:
Data fitting force
Contextual force
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Discrete objective minimization
••• Bi-1Bi-2Bi-3
• Find linear transformation T(excluding rotation) to minimize:
Data fitting force
Contextual force
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Balance between two forces
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Emerging city of Shenzhen, China
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Results: textured buildings
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Manchester Civil Justice Centre (Manchester, UK)Habitat 67 (Montreal, Canada)The Crooked House (Sopot, Poland)
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(Thank You)!