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IIIT Procedural 3D Building Reconstruction using Shape Grammars and Detectors Markus Mathias Anđelo Martinović Julien Weissenberg Luc Van Gool May, 19 th 2011

Procedural 3D Building Reconstruction using Shape Grammars … · 2013-02-20 · IIIT Procedural 3D Building Reconstruction using Shape Grammars and Detectors Markus Mathias Anđelo

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IIIT

Procedural 3D Building Reconstruction

using Shape Grammars and Detectors

Markus Mathias

Anđelo Martinović

Julien Weissenberg

Luc Van Gool

May, 19th 2011

IIIT2/31

System Overview

Colonnade --> split(x){ Column | { Column }* | Column } Column --> split(y){ ~1 : Shaft | Capital }

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System Overview

Colonnade --> split(x){ Column | { Column }* | Column } Column --> split(y){ ~1 : Shaft | Capital }

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System Overview

Colonnade --> split(x){ Column | { Column }* | Column } Column --> split(y){ ~1 : Shaft | Capital }

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System Overview

Colonnade --> split(x){ Column | { Column }* | Column } Column --> split(y){ ~1 : Shaft | Capital }

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System Overview

Colonnade --> split(x){ Column | { Column }* | Column } Column --> split(y){ ~1 : Shaft | Capital }

Inverse ProceduralModeling System

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System Overview

Colonnade --> split(x){ Column | { Column }* | Column } Column --> split(y){ ~1 : Shaft | Capital }

Inverse ProceduralModeling System

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Modelling: Procedural Modelling

• Create models from set of rules separate from evaluation engine

• Generating large scenes with only a few amount of rules

• Models are compact and semantically meaningful

• Used for plant modelling, façades and even entire cities including street network, building, façades, trees

• BUT: Focus so far mostly on creation of new virtual buildings, not existing ones

Images courtesy of www.procedural.com

IIIT9/31

Pictures

Grammar

?

How can we use prior knowledge encoded by the grammar?

Inverse Procedural Modeling

IIIT10/31

Pictures

Grammar

How can we use prior knowledge encoded by the grammar?

Asset detectors!

Asset detectors relate image areas with grammar symbols

Inverse Procedural Modeling

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GrammarInterpreter

VisionModule

AssetDetectors

3D ReconstructionModule

Inverse Procedural Modeling System

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GrammarInterpreter

VisionModule

AssetDetectors

3D ReconstructionModule

Inverse Procedural Modeling System

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Grammar Interpreter

Analyzes input grammar Extracts semantic information CGA shape:

Standardized description Powerful shape operations Readable by humans Tool for rendering available (Cityengine)

GrammarInterpreter

VisionModule

Symbol listMatching symbols

Structural InformationEstimated attributes

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Temple Grammar Example

Colonnade --> split(x){ ~ columnSpacing : Column | { ~ columnSpacing : Column }*

| ~ columnSpacing : Column }

Column --> split(y){ ~1 : Shaft | capitalHeight : Capital }

Capital --> split(y){ ~echinusPartsHeight: i(echinusAsset) | ~abacusPartsHeight : i(abacusAsset) }

Shaft --> i(shaftAsset)

Colonade

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GrammarInterpreter

VisionModule

AssetDetectors

3D ReconstructionModule

Inverse Procedural Modeling System

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Asset Detector

Felzenszwalb's part based Detector is trained on a diverse set of examples, e.g. Doric, Corinthian and Ionic capitals

High confidence detections through the reconstruction process Retraining of the detector including these detections leads to

specialized detector

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GrammarInterpreter

VisionModule

AssetDetectors

3D ReconstructionModule

Inverse Procedural Modeling System

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3D reconstruction module

Using SfM web service ARC3D:● Sparse point-cloud● Camera calibrations ● Only used to support our system and not providing parts

for output model

ARC3D

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GrammarInterpreter

VisionModule

AssetDetectors

3D ReconstructionModule

Inverse Procedural Modeling System

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Vision Module

Estimation of the dominant facade planes

Re-weighting of detections:

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Vision Module

Accumulation of detections in 3D

Parameter estimation for Grammar Interpreter● Lot size, Asset size, Repeat distances, asset color

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Vision Module

Substantiation of the semantic information coming from the grammar interpreter:

● Spatial relation of detected assets

● Similarity detection if asset appears in “repeat”

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Vision module - Results

Determined parameters are translated by the grammar interpreter to the appropriate grammar attributes:

Detections with high confidence are included into the detector training set:

189+188 (new detections) for capitals 204+124 (new detections) for shafts Specialized column and shaft detector for Greek Doric temples: Detection rate for capitals increased by 7.31% (FP at 2.2) Detection rate for shafts increased by 14.89% (FP at 5.4)

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Results – Temple of Poseidon

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Results – Temple of Poseidon

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Results – Temple of Athena

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Results – Temple of Athena

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Results – Parthenon Replica in Nashville

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Results – Parthenon Replica in Nashville

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Future Work

Registration of model to the point-cloud: Measurement of the accuracy of the estimated model Adjustment of remaining parameters

Extension to use the system on houses of different styles: Haussman Neo-classical Renaissance

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Questions?