Architecture based modeling of buildings
Filip Van den Borre
2
Overview
Objective
GeoAutomation data set
Fit ground plane model
Fit facade planes
Style grammar
rjMCMC
Future work
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
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:
6
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:
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
Point cloud of Sint-Truiden
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Close-up
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) .
16
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)
Fit ground level
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Fit wall planes
Assumption: Walls // z-axis
Remove ground points
Find median point (x m ,y m ) in each voxel
Give each point a gaussian weight with μ= ||p i -m|| and σ = 1 meter
Weighted Singular Value Decomposition (SVD)
on each voxel
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, . . .
20
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 S 1 /S
2< thresholds are removed
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
Fit wall planes
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Textured model
Textured model
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Textured model
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
