Improving architectural 3D reconstruction by constrained modelling
Abstract
This doctoral thesis presents new techniques for improving the structural quality of
automatically-acquired architectural 3D models. Common architectural properties such
as parallelism and orthogonality of walls and linear structures are exploited. The locations
of features such as planes and 3D lines are extracted from the model by using
a probabilistic technique (RANSAC). The relationships between the planes and lines
are inferred automatically using a knowledge-based architectural model. A numerical
algorithm is then used to optimise the position and orientations of the features taking
constraints into account. Small irregularities in the model are removed by projecting
the irregularities onto the features. Planes and lines in the resulting model are therefore
aligned properly to each other, and so the appearance of the resulting model is
improved. Our approach is demonstrated using noisy data from both synthetic and real
scenes.