Semantic 3D models of buildings encode the geometry as well as the identity of key components of a facility, such as walls, floors, and ceilings. Manually constructing such a model is a time-consuming and error-prone process. Our goal is to automate this process using 3D point data from a laser scanner. Our hypothesis is that contextual information is important to reliable performance in unmodified environments, which are often highly cluttered. We use a Conditional Random Field (CRF) model to discover and exploit contextual information, classifying planar patches extracted from the point cloud data. We compare the results of our context-based CRF algorithm with a context-free method based on L 2 norm regularized Logistic Regression (RLR). We find that using certain contextual information along with local features leads to better classification results.
Labeling of planar patches in a point cloud using context is performed in REF .
Using context to create semantic 3d models of indoor environments
in BMVC
Computer Science