This research addresses the problem of efficiently and robustly reconstructing semantically-rich 3D architectural models from laser-scanned point-clouds. It first covers the pre-existing literature and industrial developments in active-sensing, 3D reconstruction of the built-environment and procedural modelling. It then documents a number of novel contributions to the classical problems of change-detection between temporally varying multi-modal geometric representations and automatic 3D asset creation from airborne and ground point-clouds of buildings. Finally this thesis outlines on-going research and avenues for continued investigation - most notably fully automatic temporal update and revision management for city-scale CAD models via data-driven procedural modelling from point-clouds. In short this thesis documents the outcomes of a research project whose primary aim was to engineer fast, accurate and sparse building reconstruction algorithms.Formally: this thesis puts forward the hypothesis (and advocates) that architectural reconstruction from actively-sensed point-clouds can be addressed more efficiently and affording greater control (over the geometric results) - via deterministic procedurally-driven analysis and optimisation than via stochastic sampling.
|Date of Award||2018|
|Supervisor||Paul Shepherd (Supervisor) & Matthew Brown (Supervisor)|
- 3D-Building-Reconstruction, Laser-Scanning, Procedural-Modelling