Deducing the Location of Glass Windows in 3D Indoor Environments

Abstract

Over the years, the pace at which data is generated keeps on increasing. As a consequence, the data itself no longer holds the highest value, but rather the information and context the data captures are. This principle also holds in the 3D environment modelling scene, as accurately depicting an environment holds more value than the number of models there are of it.

One of the major problems in 3D environments, especially when the environment represents a building, is the presence of glass. A lot of the data captured to model these 3D environments is captured using LiDAR laser scanning. This is where glass becomes a problem as glass is almost completely transparent to laser beams at the typical wavelengths used when using LiDAR laser scanning. As a consequence, glass can lead to problems with navigational routes as it is invisible in the environment but still blocks the path. It can also create false spaces in the captured environment as it can also partially act as a mirror reflecting the laser beam and showing these reflections in space as if they were captured in a straight line.

Alternative manners for capturing and identifying glass in environments captured with laser have been created over the years, but they often need a dedicated set-up, expensive equipment or a lot of data. These solutions are not always feasible for users of point cloud data.

Therefore, in this thesis a focus is put on how can a low entry solution be created for this problem, which leads to the main research question: How can the location of glass be deduced using only information acquired from 3D point clouds and a reference position?

To answer this question, this thesis focuses on the deduction of the locations of glass windows in the provided input. To find these a projection from 3D data to 2D is performed. In 2D image space, contours are then detected that match the criteria of window frames. These contours are then used to segregate parts of the 3D point cloud that should contain the window detected in the projection. After clustering these parts and the best matching cluster is deduced to be a window.

In this thesis, it is shown that using the proposed methodology it is possible to deduce the location of glass in a LiDAR point cloud using only an additional reference position, but there are some flaws with the simplified input of the method.