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Ground and aerial meta-data integration for localization and reconstruction: A review
- Source :
- Pattern Recognition Letters. 127:202-214
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- Localization and reconstruction are two highly related research areas. Both of them have developed rapidly in recent years. Apparently, with the help of ground and aerial meta-data integration, the performance of both localization and reconstruction can go a step further. For localization, aerial meta-data provides a global reference, by which the ground query can achieve a cumulative error free absolute localization. As for reconstruction, a complete and detailed model can be reconstructed by integrating ground and aerial meta-data. Though with many advantages, the integration itself is non-trivial. It is difficult to obtain ground-to-aerial correspondences neither in 2D manner nor in 3D manner. That is because: (1) The differences between the ground and aerial images in viewpoint, scale, illumination, etc. are notable; (2) The discrepancies between the ground and aerial point clouds in terms of point density, accuracy, noise level, etc. are very large. To deal with these problems, lots of methods have been proposed recently. In this paper, the methods of integrating ground and aerial meta-data for localization and reconstruction are reviewed respectively. Though many intermediate results with high quality have been achieved, we hope that inspired by the reviewed methods in this paper, more thorough methods and impressive results would emerge.
- Subjects :
- Computer science
business.industry
Point cloud
02 engineering and technology
01 natural sciences
Metadata
Artificial Intelligence
0103 physical sciences
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Computer Vision and Pattern Recognition
Artificial intelligence
010306 general physics
Scale (map)
business
Software
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 127
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi...........0c223b85202147d395af73dcc22d8d3d