On the Automatic Reconstruction of Building Information Models from Uninterpreted 3D Models

Department of Geoinformation Science

Technische Universität Berlin

2009/07/29

On the Automatic Reconstruction of

Building Information Models from

Uninterpreted 3D Models

Thomas H. Kolbe

Director of theInstitute for Geodesy and Geoinformation ScienceBerlin University of Technology

Joint work with Claus Nagel & Alexandra Stadler{kolbe | nagel | stadler}@igg.tu-berlin.de

Academic Track of Geoweb 2009 Conference, Vancouver

23/1/20082 T. H. Kolbe – Semantische 3D-Stadtmodelle für geschäftliche Kommunikation


Building Information Models & IFC

Building Information Model (BIM) digital representation of the physical and functional

characteristics of a constructed site or facility comprehensive information source on a facility

aiming at collaborative usage intended to be used along the entire lifecycle of a facility key feature: models have well-defined semantics

Industry Foundation Classes (IFC) ISO standard for semantic building models diverse crafts/themes; incl. billing of material and costs supported 3D geometry types: CSG, Sweep, B-Rep, etc.

2009/07/293 Automatic reconstruction of building information models from uninterpreted 3D models


BIM Application #1: Energy Assessment

Image: ThermoRender, Nemetschek North AmericaImage: ThermoRender, Nemetschek North America



BIM Application #2: Space Management

Image: Space planning created with Onuma Planning SystemImage: Space planning created with Onuma Planning System



BIM Application #3: Structural Analysis

Image: Autodesk Robot Structural Analysis BrochureImage: Autodesk Robot Structural Analysis Brochure



Problem Statement

BIM models are typically prepared for newly planned buildings only

But: applications should also be usable with existing buildings

Acquisition method required for BIM models for existing buildings

manual acquisition is expensive automation required

Challenges:What are appropriate data sources?

Which problems have to be faced and how could they be

overcome concerning the interpretation / reconstruction?



Starting Point: 3D Geometry / Visual Models

Photogrammetric models

Airborne laser scan models

CAD and planning models

Visualization models

Preprocessed sensor data from LIDAR / Preprocessed sensor data from LIDAR / Photogrammetry, i.e. point clouds or surface patchesPhotogrammetry, i.e. point clouds or surface patches

Visual models / surface based models (‘Visual models / surface based models (‘polygon soupspolygon soups’)’) From CAD or computer graphicsFrom CAD or computer graphics

Characteristics of input data:Characteristics of input data: Pure geometry (and radiometry)Pure geometry (and radiometry) Geometry can be unstructured or structured according Geometry can be unstructured or structured according

to visualization purposes; it can also be incompleteto visualization purposes; it can also be incomplete Topological errors (permeations, overshoots, Topological errors (permeations, overshoots,

undershoots)undershoots) No semantic informationNo semantic information



Goal: Reconstruction of BIM models

Reconstructed BIM models

explain (most of) the observed geometrical entities in the ‘best’ way

are composed of fully classified and attributed entities like Walls, Slabs, Roofs, Spaces, etc.

thus, they are semantically rich and structured

semantics follow the IFC standard

have volumetric, parametric geometries (CSG) required in order to make the models editable by CAD tools

should make hypotheses about 3D components with respect to invisibility / unobservability



IFC Example



Surface-based modeling of Interior Space



Two-stage Reconstruction Process

Automatic reconstruction of BIM models from 3D geometry models faces a high level of complexity

Unstructured, uninterpreted geometry semantic classification

handled in Stage 1:graphics model semantically enriched boundary model

Accumulative generative modelling paradigm

handled in Stage 2:semantically enriched boundary model building information model with volumetric, parametric components



Urban Models and their Applications



CityGML

OGC Standard for virtual 3D city models

spatial and

thematic disaggregation /

semantic modeling

LOD4: Building model including interior space

Modeling paradigm: BRep + explicit semantics

Close to photogrammetric / lidar observations

In fact, closer than IFC models

using CSG



Stage 1: Graphics model CityGML

= classification stage Purely geometric graphics models (e.g., KML) are converted to

semantically enriched boundary models (e.g., CityGML)



Underneath the surface…

Visual models are explicitly built for visualisation only visible parts are trustworthy

Wireframe model

dormers are extrudedthrough the whole building

Textured visualisation

visualisation does not reveal over-lapping building and dormer

bodies



Strategies for Geometry Handling (I)



Geometry remains unchanged Merely attach semantic information to polygons

Transform ‘polygon soup‘ into structured geometry aggregates No effect on coordinate values

New geometry is generated according to target model and fitted to observations May result in topological changes (e.g. closing of volumes)

New geometry is generated according to target model and fitted to observations After geometric/semantic structuring keep original geometry in final result

Strategies for Geometry Handling (II)

A Keep original geometry

B Structure geometry

C Replace geometry

D Additional requirements on the target model



Level-of-Detail (LOD) Concept

How to decide on the appropriate target model LOD?

Conclusions about target model LOD according to input model granularity E.g., no window setoffs or molded roof structures ≤ LOD2

User specifies target model LOD Attention: input model may not fulfill requirements of the chosen LOD

Specification of one basic target LOD (automatic recognition or user input) Build LOD series covering all lower LODs (probably using generalization) Explicit linkage between multiple LOD representations

Automatic LOD recognition

User input

Build a LOD series



Stage 2: CityGML IFC

Reconstruction of component-based volume model from a surface model

Instantiation and rule-based combination of volumetric building objects (walls, roofs, …) which most likely explain the input model

CityGML model is seen as the observation, IFC model will be the interpretation result

Key aspect: Semantic information as a priori knowledge Both CityGML and IFC provide semantic models of the built environment Allows for reducing the search space of potential IFC elements

Complexity results from the fact that CityGML and IFC follow different modeling paradigms Building components are only observable in parts or not observable

typ. only observable parts are contained in input 3D model From each component two or more surfaces may be observable

Represented as individual semantic entities in CityGML



Differing Modeling Paradigms

Volumetric, parametric primitives representing the structural components of buildings

IfcWallStandardCase

IfcBeam

IfcSlabIfcWindow

BIM (e.g., IFC) Constructive Solid

Geometry

Accumulation of observable surfaces of topographic features

WallSurface

InteriorWallSurface

FloorSurface

IntBuildingInstallation

GroundSurface

Window

3D GIS (e.g., CityGML)

Boundary Representation



Matching between CityGML and IFC Entities

n CityGML entities may represent one IFC element

n CityGML entities may result in m competing IFC elements

Further 1:1 and 1:m relations possible High combinatorial complexity

Generation of IFC element hypotheses from CityGML entities Semantic information as a priori knowledge

Evaluation of geometric-topological relations between CityGML entities



Instantiation of IFC Elements (I)

Different wall connections result in ambigious CSG representations

Instantiation of CSG primitives which best fit the spatial properties of all matched CityGML entities

Man-made objects often deviate from the idealized CSG shape Parameter estimation has to obey contextual constraints

Unary: usually impair the best fit of a single element

Mutual: aim at aligning elements affect parameters of many elements

Conversion of B-Rep to CSG in general is ambiguous Building components are only observable in parts

CSG primitives cannot be derived from closed volumes

Competing hypotheses Requires additional

a priori knowledge / assumptions



Instantiation of IFC Elements (II)

Purely geometric-topological constraints on IFC primitives cannot prevent unreasonable element hypotheses

E.g., IfcRoof elements at the bottom of a building

Both CityGML and IFC do not explicitly qualify objects and inter-object relations in order to ensure sensible configurations

Based on UML, XSD, and EXPRESS

Focus on generic notion of ‘objects’ and ‘associations’

Reconstruction requires a framework providing enhanced model expressiveness

Physical, functional, semantic / logical object constraints

Rules for structural valid element configurations

What makes a ‘valid’ building?



Interpretation Strategy (I)

How to express / formalize knowledge about 3D building models?

CityGML and IFC data models do not provide (formal) constraints on object instances

A more expressive formal representation is required specifying how complex objects are aggregated in a logically / semantically sound way

Formal Grammars are becoming applied increasingly often for this purpose

Formal grammars originate from computational linguistics

Definition of different classes of grammars by N. Chomsky;

later extended by D. Knuth (attributed grammars)



Example of a Formal Grammar (in EBNF)



Interpretation Strategy (II)

Requirements on the grammarWords / Objects have attributes (attribute grammar)Geometric Shapes (shape grammar / split grammar)Stochastical aspects (a priori probabilities)Combination of all grammar types is required

Further requirements: Need of an evaluation / objective function

In order to determine the ‘best’ interpretation from all possible hypotheses

meaningful definition of ‘best’ interpretation- Using probability theory: the most likely model

under the given data

Avoid overfittings



Conclusions

Reconstruction of BIM models is a specific instance of the general 3D object recognition problem

What makes it especially difficult?Gap between observed surfaces and volumes to be

reconstructed (BRep CSG ambiguities)High structural and semantic complexity of BIM modelsUncertainty, unobservability, and errorneous observationsDefinition of an objective function / measure to compare the

appropriateness (probability?) of BIM model hypotheses

Which aspects help in this process?Two-stage strategy allows for a step-wise interpretation and

extraction of semantic information (divide-and-conquer) IFC and CityGML are target models with well-defined semantics

Documents

On the Automatic Reconstruction of Building Information Models from Uninterpreted 3D Models