Rough Estimation of Interior Dimensions Using Structure from Motion Techniques
Nowadays structure from motion algorithms have become accurate enough to compete with laser scanner accuracy, however most of the algorithms require points of interest and textured surfaces in order to give better results. Most algorithms will have poor performance when it comes to monotonically coloured or textureless surfaces. Furthermore, the output of the algorithms will have gaps in the projection of the structure it is trying to recreate. This kind of projection would be useless in a case where consistency and completeness of surfaces is more important than the level of detail. In this thesis the author will try to use structure from motion techniques and new ideas to create a projection of an interior room which focuses on the essence of the room (I.e aspect ratio, correct oor plan) rather than on the level of detail of objects in the room. The goal of this thesis will be to create an algorithm which can generate a projection out of a sparse point cloud (result of SfM) that is consistent enough to allow it to be used for applications that require a more complete model rather than a detailed one (I.e robot pathfinding, indoor people tracking).
Graduation Thesis language
Graduation Thesis type
Master - Computer Science