Name
Abstract | ||
|---|---|---|
Visibility analysis is an important application of 3D GIS data. Current approaches require 3D city models that are often derived from detailed aerial point clouds. We present an approach to visibility analysis that does not require a city model but works directly on the point cloud. Our approach is based on the medial axis transform, which models the urban environment as a union of balls, which we then use to construct a depthmap that is used for point visibility queries. As we demonstrate through our experiments on a real-world aerial LiDAR point cloud, the main benefits of our approach are 1) it is robust to noise, irregular sampling and holes of typical aerial LiDAR datasets, 2) it gives visibility results that are significantly more accurate than the often highly generalised city models, and 3) it is a simple algorithm that is easy to parallelise. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.2312/udmv.20151342 | UDMV |
Field | DocType | Citations |
Computer vision,Visibility,Computer graphics (images),Computer science,Ball (bearing),Medial axis,Lidar,Artificial intelligence,Sampling (statistics),SIMPLE algorithm,Point cloud,3D city models | Conference | 1 |
PageRank | References | Authors |
0.35 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ravi Peters | 1 | 1 | 0.35 |
| Hugo Ledoux | 2 | 231 | 22.27 |
| Filip Biljecki | 3 | 157 | 11.33 |