Name
Abstract | ||
|---|---|---|
Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is characterized as a camera pose estimation problem. We propose a novel application of mutual information registration methods, which exploits the statistical dependency in urban scenes of optical apperance with measured LIDAR elevation. We utilize the well known downhill simplex optimization to infer camera pose parameters. We discuss three methods for measuring mutual information between LIDAR imagery and optical imagery. Utilization of OpenGL and graphics hardware in the optimization process yields registration times dramatically lower than previous methods. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithm with a collection of urban images and present 3D models created with the fused imagery. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CVPRW.2009.5206539 | CVPR: 2009 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-4 |
Keywords | Field | DocType |
lidar,solid modeling,virtual reality,image registration,layout,pose estimation,entropy,graphics hardware,meteorology,graphics,laser radar,radar imaging,point cloud,laser fusion,image fusion,mutual information,optical imaging | Computer vision,Computer science,Remote sensing,Lidar,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1063-6919 | 49 | 1.75 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Mastin | 1 | 59 | 3.13 |
| Jeremy Kepner | 2 | 606 | 61.58 |
| John W. Fisher III | 3 | 878 | 74.44 |