Ming Li


The increasing availability of high resolution airborne imagery increases the accuracy of building modelling of urban scenes. This high accuracy of building modelling offers a strong reference for disaster recovery and asset evaluation. With the advantage of having more façade information, this thesis builds on previous efforts in building reconstruction from airborne oblique imagery.

Based on previous work, this thesis presents two schemes to construct building models from point clouds derived from oblique imagery. With the assumption that buildings are in a cubic-shape, the first scheme consists of three different steps. Plane estimation aims at identifying dominant surfaces; edge extraction helps in detecting and simplifying in-plane edges in each identified surfaces; model construction finishes the job of assembling the surfaces and edges together and producing a model in a universally accepted format. We find this scheme works well with complete point clouds that cover all sides of the building. A second method is proposed to handle the complications when the point clouds do not cover all sides of the building. The main structure of the building is estimated using minimum bounding box on the dominant planes. The rest of the estimated planes are then attached to the main structure. The process can produce a water-tight building model.

The schemes are tested on point cloud data sets from multiple sources, including both image derived and lidar derived point clouds. The surface based approach and minimum bounding box based approach both show the capability of reconstructing models, while both of them have disadvantages. The limitations such as density of point clouds; fitting accuracy; and future work, including increasing efficiency and robustness, are also discussed.

Library of Congress Subject Headings

Remote-sensing images--Data processing; Image analysis; Aerial photogrammetry--Data processing; Optical radar--Data processing

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


John P. Kerekes

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

David Messinger


Physical copy available from RIT's Wallace Library at G70.4 .L4 2015


RIT – Main Campus

Plan Codes