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.
REF uses a CRF model to discover and exploit contextual information, classifying planar patches extracted from the point cloud data.
Using context to create semantic 3d models of indoor environments
in BMVC
Computer Science