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SmartBoxes for Interactive Urban Reconstruction

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Presentation on theme: "SmartBoxes for Interactive Urban Reconstruction"— Presentation transcript:

1 SmartBoxes for Interactive Urban Reconstruction
Liangliang Nan1, Andrei Sharf1, Hao Zhang2, Daniel Cohen-Or3, Baoquan Chen1 1 Shenzhen Institutes of Advanced Technology (SIAT), China 2 Simon Fraser University, Canada 3University of Tel Aviv, Israel

2 3D Cities Virtual Philadelphia Virtual Berlin

3 Acquisition of Urban Environments
Remote Sensing Systems Airborne LIDAR Cameras/videos Auto-mounted LIDAR 3D Digital City 3 3

4 3D LiDAR scanner Street-level 60km/h 180 pitch [100-300m] range
100K points/second 5cm XY accuracy

5 Outdoor Urban Scanning

6

7 Imperfect Scans - Occlusions
Point cloud contains holes due to various occlusions (“shadows”)

8 Imperfect Scans – Angle & Range
Oblique scanning angle Laser energy attenuation on range

9 Urban Building Characteristics
Dominant planes Axis-aligned basic primitives Repetitions, intra symmetry and regularity

10 SmartBoxes Box-up and Smart!

11 SmartBoxes Box prior shape fitting Smart context awareness Data
Both

12 Live Demo

13

14 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]

15 Preprocessing Automatic detection of planes and edges assuming dominant orthogonal axes RANSAC planes Line sweep edges

16 Snapping a Box 2D rubber band ROI Collect planes, edges, corners
Find the best fitting box using data fitting force D(B,P)

17 Data fitting D(B,P) snap
Data fitting force Facet Edge Data quality (Confidence + Density) Distance

18 Grouping Simple SmartBox Compound SmartBox
Align to remove gaps and intersections cluster and align close to co-linear edges

19 Drag-and-drop context C(Bi-1, Bi)
The context of Bi Interval Alignment Scale Bi Bi-3 Bi-2 Bi-1 ••• Context

20 Drag-and-drop context C(Bi-1, Bi)
The context of Bi Interval term Bi ••• Bi-3 Bi-2 Bi-1

21 Drag-and-drop context C(Bi-1, Bi)
The context of Bi Alignment term Bi ••• Bi-3 Bi-2 Bi-1

22 Drag-and-drop context C(Bi-1, Bi)
The context of Bi Scale term Bi Bi ••• Bi-3 Bi-2 Bi-1

23 Discrete objective minimization
Find linear transformation T(excluding rotation) to minimize: Data fitting force Contextual force

24 Discrete objective minimization
Find linear transformation T(excluding rotation) to minimize: Data fitting force Contextual force ••• Bi-3 Bi-2 Bi-1

25 Balance between two forces

26 Emerging city of Shenzhen, China

27 Results: textured buildings

28 The Crooked House (Sopot, Poland)
Manchester Civil Justice Centre (Manchester, UK) Habitat 67 (Montreal, Canada)

29 (Thank You)!


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