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Architecture based modeling of buildings

Filip Van den Borre

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Overview

Objective GeoAutomation data set Fit ground plane model Fit facade planes Style grammar rjMCMC Future work

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Objective

Model buildings Without human interaction Input: images Semantic model: knowledge about the structure of

the building and his elements. Use architectural grammar: set of rules that can be

used to describe the building model Repeated elements -> one enhanced texture for each

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Objective

Procedural Modeling of Buildings. [Mueller et al.] SIGGRAPH 2006

CGA shape: shape grammar for the procedural modeling of CG architecture. low cost -> computer games, movies, ... efficient unprecedented level of detail

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Example PRIORITY 1: 1: footprint ; S(1r,building height,1r) facades T(0,building height,0) Roof(”hipped”,roo f angle){ roof } PRIORITY 2: 2: facades ; Comp(”sidefaces”){ facade } 3: facade : Shape.visible(”street”) ; Subdiv(”X”,1r,door width*1.5){ tiles | entrance } : 0.5 ; Subdiv(”X”,door width*1.5,1r){ entrance | tiles } : 0.5 4: facade ; tiles 5: tiles ; Repeat(”X”,window spacing){ tile } 6: tile ; Subdiv(”X”,1r,window width,1r){ wall | Subdiv(”Y”,2r,window height,1r){ wall | window | wall } | wall } 7: window : Scope.occ(”noparent”) != ”none” ; wall 8: window ; S(1r,1r,window depth) I(”win.obj”) 9: entrance ; Subdiv(”X”,1r,door width,1r){ wall | Subdiv(”Y”,door height,1r){ door | wall } | wall } 10: door ; S(1r,1r,door depth) I(”door.obj”) 11: wall ; I(”wall.obj”) PRIORITY 3: ...... 14:

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My mission

PRIORITY 1: 1: footprint ; S(1r,building height,1r) facades T(0,building height,0) Roof(”hipped”,roo f angle){ roof } PRIORITY 2: 2: facades ; Comp(”sidefaces”){ facade } 3: facade : Shape.visible(”street”) ; Subdiv(”X”,1r,door width*1.5){ tiles | entrance } : 0.5 ; Subdiv(”X”,door width*1.5,1r){ entrance | tiles } : 0.5 4: facade ; tiles 5: tiles ; Repeat(”X”,window spacing){ tile } 6: tile ; Subdiv(”X”,1r,window width,1r){ wall | Subdiv(”Y”,2r,window height,1r){ wall | window | wall } | wall } 7: window : Scope.occ(”noparent”) != ”none” ; wall 8: window ; S(1r,1r,window depth) I(”win.obj”) 9: entrance ; Subdiv(”X”,1r,door width,1r){ wall | Subdiv(”Y”,door height,1r){ door | wall } | wall } 10: door ; S(1r,1r,door depth) I(”door.obj”) 11: wall ; I(”wall.obj”) PRIORITY 3: ...... 14:

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Data of GeoAutomation

Spin-off company of Visics System of precise mapping measurements by means

of mobile mapping Vehicle with 10 cameras 3D feature points clouds Geo-referenced by reference to ground control

points Gravity vector g || z-axis x, y, z in meters

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Different cameras

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Point cloud of Sint-Truiden

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Close-up

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Preprocessing

Feature points located at great distances from the cameras have a low precision

They create noise with respect to other points When only the correct points are retained, there

are not enough of them to provide proof for possible models

Remove the feature points if distance > 20 m

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Split data up into voxels

No relation between feature points at a certain distance from each other

Easier to access & analyse the data Voxel size has the same order of magnitude as

the minimal size of the structures we want to find in the first steps (In the first steps we want to locate the ground and the facades without the door and windows structures).

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Fit ground level

Assumption: visual point with the smallest z-value = point on the ground plane

If (lowest point ≠ ground plane) then Point is an outlier There are no feature points on the ground plane

Carry out a median filtering to correct errors (lowest point ≠ ground plane)

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Fit ground level

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Fit wall planes

Assumption: Walls // z-axis Remove ground points

Find median point (xm,y

m) in each voxel

Give each point a gaussian weight with μ= ||pi-m|| and σ = 1 meter

Weighted Singular Value Decomposition (SVD) on each voxel

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Fit wall planes

Problems: Corner (two walls coming together) No walls

Plane-shaped objects like cars, vans, fences, . . . Street furniture: trees, (traffic) lights, traffic signs, . . .

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Fit wall planes

Solutions: The separate walls of a corner are found by looking at

the walls in the neighboring voxels Errors caused by plane-shaped objects will be

removed by projecting the images on the planes or by using car-detectors, ...

The singular values give us an idea of how plane – alike a given point cloud is

Results with S1/S

2 < thresholds are

removed

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Fit wall planes

Facade planes <-> all other objects No feature points behind the facade plane Count all points after a plane If (#points ~ 0) -> facade plane

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Fit wall planes

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Fit wall planes

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Textured model

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Textured model

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Textured model

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Textured model

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Future work

Join facade planes to a 3D shape Find smaller 3D structures like doors & windows Find description/model based on rules Perform in-painting of hidden structures based on

the resulting model and the images . . .