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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:767574 |
| Date | January 2018 |
| Creators | Edum-Fotwe, Kwamina |
| Contributors | Shepherd, Paul ; Brown, Matthew |
| Publisher | University of Bath |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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