Transforming an Adjacency Graph into Dimensioned Floorplan Layouts. (1st March 2022)

• Record Type: Journal Article
• Authors: Bisht, Sumit; Shekhawat, Krishnendra; Upasani, Nitant; Jain, Rahil N.; Tiwaskar, Riddhesh Jayesh; Hebbar, Chinmay
• Is Part Of: Computer graphics forum. Volume 41:Number 6(2022)
• Page Start: 5
• Page End: 22
• Publication Date: 2022-03-01
• Subjects: computational geometry -- modeling -- CAD; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14451
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: In recent times, researchers have proposed several approaches for building floorplans using parametric/generative design, shape grammars, machine learning, AI, etc . This paper aims to demonstrate a mathematical approach for the automated generation of floorplan layouts. Mathematical formulations warrant the fulfilment of all input user constraints, unlike the learning‐based methods present in the literature. Moreover, the algorithms illustrated in this paper are robust, scalable and highly efficient, generating thousands of floorplans in a few milliseconds. We present G2PLAN, a software based on graph‐theoretic and linear optimization techniques, that generates all topologically distinct floorplans with different boundary rooms in linear time for given adjacency and dimensional constraints. G2PLAN builds on the work of GPLAN and offers solutions to a wider range of adjacency relations (one‐connected, non‐triangulated graphs) and better dimensioning customizability. It also generates a catalogue of dimensionless as well as dimensioned floorplans satisfying user requirements. Abstract : In recent times, researchers have proposed several approaches for building floorplans using parametric/generative design, shape grammars, machine learning, AI, etc .