Skip to main content

Structural regularity learning for automated architecture modeling

Resource type
Thesis type
(Thesis) Ph.D.
Date created
This thesis studies the perception and reasoning of geometric elements in architectural modeling. We build a reconstruction and generation system for more faithfully reconstructing building models from sensor data or generating realistic floorplan models with user controls. The technical challenges include understanding basic geometric primitives (e.g., points, lines, planes) and their relationships (e.g., parallelism, perpendicularity, angles between lines, distances between primitives), that are easily interpretable by humans. We investigate 1) non-learnable techniques for assembling buildings geometric primitives; 2) neural relational architectures for inferring building topology; 3) neural generative architectures for creating house layouts under user constraints; 4) incorporating more realistic user constraints for obtaining production-level performance on generating house layouts; and 5) the unsupervised discovery of structural regularities for architecture modeling.
118 pages.
Copyright statement
Copyright is held by the author(s).
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Furukawa, Yasutaka
Thesis advisor: Mori, Greg
Member of collection
Download file Size
etd22188.pdf 22.55 MB

Views & downloads - as of June 2023

Views: 0
Downloads: 0