TY  - JOUR
A1  - Stojanovic, Vladeta
A1  - Trapp, Matthias
A1  - Richter, Rico
A1  - Döllner, Jürgen Roland Friedrich
T1  - Service-oriented semantic enrichment of indoor point clouds using octree-based multiview classification
JF  - Graphical Models
N2  - The use of Building Information Modeling (BIM) for Facility Management (FM) in the Operation and Maintenance (O&M) stages of the building life-cycle is intended to bridge the gap between operations and digital data, but lacks the functionality of assessing the state of the built environment due to non-automated generation of associated semantics. 3D point clouds can be used to capture the physical state of the built environment, but also lack these associated semantics. A prototypical implementation of a service-oriented architecture for classification of indoor point cloud scenes of office environments is presented, using multiview classification. The multiview classification approach is tested using a retrained Convolutional Neural Network (CNN) model - Inception V3. The presented approach for classifying common office furniture objects (chairs, sofas and desks), contained in 3D point cloud scans, is tested and evaluated. The results show that the presented approach can classify common office furniture up to an acceptable degree of accuracy, and is suitable for quick and robust semantics approximation - based on RGB (red, green and blue color channel) cubemap images of the octree partitioned areas of the 3D point cloud scan. Additional methods for web-based 3D visualization, editing and annotation of point clouds are also discussed. Using the described approach, captured scans of indoor environments can be semantically enriched using object annotations derived from multiview classification results. Furthermore, the presented approach is suited for semantic enrichment of lower resolution indoor point clouds acquired using commodity mobile devices.
KW  - Semantic enrichment
KW  - 3D point clouds
KW  - Multiview classification
KW  - Service-oriented
KW  - Indoor environments
Y1  - 2019
U6  - https://doi.org/10.1016/j.gmod.2019.101039
SN  - 1524-0703
SN  - 1524-0711
VL  - 105
PB  - Elsevier
CY  - San Diego
ER  -