Fast and extensible building modeling from airborne LiDAR data
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Variational model-based 3D building extraction from remote sensing data
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
2.5D dual contouring: a robust approach to creating building models from Aerial LiDAR point clouds
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
An Automation System of Rooftop Detection and 3D Building Modeling from Aerial Images
Journal of Intelligent and Robotic Systems
Learning the compositional structure of man-made objects for 3D shape retrieval
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
Detection, classification and estimation of individual shapes in 2D and 3D point clouds
Computational Statistics & Data Analysis
Visually-complete aerial LiDAR point cloud rendering
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Modeling residential urban areas from dense aerial LiDAR point clouds
CVM'12 Proceedings of the First international conference on Computational Visual Media
Automatic 3d city reconstruction platform using a LIDAR and DGPS
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
| Hi-index | 0.00 |
This paper presents a method to detect and construct a 3D geometric model of an urban area with complex buildings using aerial LIDAR (Light Detection and Ranging) data. The LIDAR data collected from a nadir direction is a point cloud containing surface samples of not only the building roofs and terrain but also undesirable clutter from trees, cars, etc. The main contribution of this work is the automatic recognition and estimation of simple parametric shapes that can be combined to model very complex buildings from aerial LIDAR data. The main components of the detection and modeling algorithms are (i) Segmentation of roof and terrain points. (ii) Roof topology Inference. We introduce the concept of a roof-topology graph to represent the relationships between the various planar patches of a complex roof structure. (iii) Parametric roof composition. Simple parametric roof shapes that can be combined to create a complex roof structure of a building are recognized by searching for sub-graphs in its roof-topology graph. (iv) Terrain Modeling. The terrain is identified and modeled as a triangulated mesh. Finally, we provide experimental results that demonstrate the validity of our approach for rapid and automatic building detection and geometric modeling with real LIDAR data. We are able to model cities and other urban areas at the rate of about 10 minutes per sq. mile on a low-end PC.