Flow with corridor: A GAN-based floorplan generator

“Flow along the corridor”

A GAN-based floor plan generator

Intro Case Study

Research Objective

Methodology

Dataset Generation

Training

Conclusion

Abstract

Generative design has always been a very hot topic in recent times. Over the past few years, machine  learning tools have emerged as invaluable aids in the creation of more precise and diverse 2D or 3D  building models, fostering greater user engagement in the design process. A retrospective examination of  previous approaches shows that techniques such as diffusion models, generative adversarial networks  (GAN), graph neural networks (GNN) and etc. They all attempt to educate computer to understand the  complexity of a building and reconstruct it.

This research will continue this topic and try to develop another method to let the ML understand, learn  and reconstruct the information in the building plan to develop a generator. The training results  allow users to customize the generation. In addition, it relies on the necessary circulation lines in the  building (ie, corridor space) to generate a reasonable building plan. After verifying the possibility of this methodology, this study propose a feasible solution for  subsequent generation of architectural 3D space.

Key words：generative design, Pix2Pix model, spatial relationship, Graph theory.

ML

Case Study

1, 3D GAN_Voxel based: Synthesis and generation for 3D architecture volume  with generative modeling

User input: /

Scope of application: building geometry

Training model：3D GAN

Dimension: 3D

Limitation: Generate results with low resolution, can  only simply learn the shape characteristics of buildings.

Case Study

2, Graph based: Building GAN

User input: function, graph

Scope of application: single apartment

Training model：GNN+pointer-based cross-modal modules

Dimension: 3D

Advantage: can generate public building with internal spatial  divisions.

Case Study

3, 2D GAN: ArchiGan

User input: boundary

Scope of application: single apartment

Training model：cGAN (pix2pix)

Dimension: 2D

Limitations: Generate pixel-result non-vector images

Case Study

4, GNN+GAN: Graph2plan

User input: entrance, relation of rooms, boundary,  selected room function.

Scope of application：single apartment

Training model：GNN+GAN

Dimension: 2D

Advantage: Allows users to generate interactively,  increasing the controllability of generated results.

Literature Review_3D generation

Agenerative  architecturaland  urbandesignmethod  throughartificial  neuralnetworks

Synthesisand  generationfor3D  architecturevolume  withgenerative  modeling

Buildingmassing  generation usingGANtrainedon  Dutch

3Dcitymodels

UrbanLayout  WorkflowUtilizing

Generative  AdversarialNetwork  (GAN)

Building-GAN

Encodedpoints  andsurfaces

GNN+pointer-based  cross-modalmodules

surrounding

voidspace

withdifferent  information

3Durbanblock  model

Voxelbuilding  modelwithinternal  spatialdivisions

singlepublicbuilding  withinternalspatial  divisions

Literature Review_2D generation

boundary bothsingleandlarge  apartment roomconfigurationwith  detailedfurniture

Architecturallayout  designthroughdeep  learningandagentbasedmodeling:A  hybridapproach

Pix2Pix+cGAN boundary/room  relationship/roomarea

House-GAN++ GANs+  Convolutional  message  passing

roomrelationship

roomconfiguration

boundary/room  relationship/room  location

HouseDiffusion roomrelationship

usionModel

Graph-Constrained  HouseGeneration graphTransfor mer roomrelationship

Pix2pix corridorline/room  area/room  relationship(optional)

roomconfigurationwith  doorposition

roomconfigurationwith  doorposition roomconfigurationwith  doorandwindow  position roomconfiguration

bothsingleapartment  andpublicbuildings

Research Objective

Initial idea/ Spatial feature/ Limitation/ Ideal outcome

Research Objective

Most of the previous research started from the relationship between architectural  spaces and reconstructed a plane by letting the user decide some basic information of  the room, such as function, area, and adjacent relationship between rooms. For the  single apartment building plane, this condition enough to produce good results.

However, for other building types such as museums, shopping malls, hospitals, etc.,  traffic circulation is an important element that runs through the space. This study  attempts to introduce another condition on the basis of satisfying the basic constraints,  focusing on the close relationship between the building plane and its traffic space.

Comparison with previous studies, this research will solve this :

1, This research attempts to find a more precise method to capture building features and learn from it.

（both applies to 2d and 3d）

2, not only single apartment, but also explore the possibility to generate public buildings.

3, In addition to meeting the basic conditions for rational building generation, this study will also  introduce ‘circulation’, which is an important element of public buildings.

Public building's configuration follow with their circulation

First propose

room geometry

Adjacency Matrix: a matrix with rows and columns labeled by  graph vertices, with a 1 or 0 in position according to whether and.  are adjacent or not.

Can we use different matrix to reprsent some information?

rooms relationship between each other

room function/area

Initial workflow

UserInput

1.Drawa referencelineforcorridor.

2.Definethe grossfloorarea(sqm)

3.Definethe entrancelocation

4.Inputthe occupancyratioofdifferentfunctions

5.Inputseed:trydifferentexteriorvolumes

DatasetgenerationinGH

GHGeneration

Limitation

But when I start dataset and training , there is some problems:

Lack of real 3D building datasets

Due to the pixel limit of the pix2pix model, it cannot handle large public  buildings with more than 512 feature points.

Considering that the standard floor plan of a public building can represent the layout  of the entire building in general. Therefore, this research will first start with the  generation of 2D floor plans, verify the possibility of this methodology, and propose a  feasible solution for subsequent generation of architectural 3D space.

What's the outcome?

A plan generator that flows with the corridor

user input(constrains) expected outcome

1. Draw a reference line for corridor

2. Define the function and area you want

room01: 28sqm

room02: 7sqm

room03: 13sqm

room04: 18sqm

room05: 16sqm

room06: 14sqm

3. Define the relation of these rooms if you need

Plan configuration result

Methodology

Methodology_pix2pix model

2D GAN, unsupervised learning, allows learning of adjacency matrix images. pairs of data, with clear output and input, allowing the establishment of a  connection between constraint and outcome.

2D GAN architecture

Methodology_ workflow

roomlabel,roomshape,roomarea,relationbetweenrooms

Datasetgeneration

B200 Input

Labelmatrix Distancematrix Adjacencymatrix

Ground truth

Pix2pix Model Predictionimage

Labelmatrix Distancematrix Adjacencymatrix

Dataset generation Data

generation/ Feature embed

First consider_open dataset

Without labeled corridor information. Lack of dataset on public building plans

Embed feature

Here we use nodes and edges to depict a building information.

Nodes connections(yes/no) room

room label, room shape, room area, relation between rooms

Distance matrix

Adjacency matrix

Got input dataset

Output dataset (result from plugin)

area7=20

area4=16

area6=23

area5=16

Area/room label

area4=16

Obtain corresponding Input dataset

area7=20

area5=16

area2=9

area3=25

area1=17

area6=23

area2=9

area3=25

IfuseKangarootosimulatetheseconstrains  andgenerateresult,youcan'tgetperfect  result.

area1=17

Spatial Adjacency

User Input representation

1. Draw a reference line for corridor

2. Define the function and area you want

room01: 28sqm

room02: 7sqm

room03: 13sqm

room04: 18sqm

room05: 16sqm

room06: 14sqm

3. De

Dataset Variation

Around 2000 pairs of data with different area, rooms and configuration

Training

Prediction results/ Reconstruction/ Problems

Training start

Distance matrix

Step:8999

Generator loss: 8.926547394154905

Generator l1 loss: 5.807867540837871

Discriminator loss: 3.611856884098233

Label matrix

Adjacency matrix

1st Training result _433,999

steps

Step: 433999

Generator loss: 7.69580344655172

Generator l1 loss: 4.187161721570787

Discriminator loss: 4.823407427206053

Prediction Result

Adjacency matrix

Distance matrix

Label matrix

2nd Training result

_988,999 steps

Adjacency matrix

Distance matrix

Step:988999

Generator loss: 7.494118938771732

Generator l1 loss: 3.8295014311102595

Discriminator loss: 4.693514201845212

Label matrix

Prediction Result

Adjacency matrix

Distance matrix

Label matrix

Prediction 255

Prediction 655

389

Prediction 430

User Input AI

Conclusion & Discussion

Conclusion/ Limitation/ Potential and Future work

The method used in this study allows ML to accurately capture the shape relationships of  the building plan, such as the foot points of the room, the center point of the room and the  edges which is necessary to form the shape of the room. Theoretically, most linear  geometric shapes can be expressed by the nodes and edges on their boundary. Therefore, compared with methods in some previous studies, the model after deep training  should have the ability to accurately reconstruct complex spaces.

These multiple channels in Pix2Pix model allow us to learn more layers of information

In this study, we added a new layer of channel that can identify room labels based on  adjacency matrix theory. This study verified its feasibility. This also means that it is possible  to add more layers of information to the learning object to generate more complex models.

Go back to 3D generator

Theoretically, this methodology is also applicable to the reconstruction of 3D space.

Although this article does not continue an in-depth study on this, it is worth noting: for 3D  generation, maybe we need use pix2pix HD model, which allow max 1024 feature nodes to  depict a building. Of course, 3D space generation is more complicated than 2D. Whether the  coding method needs to be modified requires some further studies.

Limitation

1, Pix2pix HD Model can deal with the image size of 1024 dpi. This generator  cannot generate too big or too complex 3D buildings which has more than  1024 feature nodes.

2, This method has relatively high requirements for the depth of model

training. In the adjacency matrix, a small pixel error will cause a relatively large  deviation. And obtaining an accurate distance matrix often takes longer to  learn.

1, The previous research has shown that this method is feasible. Next, we can  increase the training time or find a dataset that is more consistent with the actual  situation and retrain the model to make the results more ideal.

2, Not only for the floor plan. How to use this method in the generation of 3D  space requires further exploration.

3, It allows us to add more channels for different information. So, change or add  more different constraints( sun hours, surrounding), let users control or interact  with generator more intelligent.

THANKS !