Semantically-Aware Aerial Reconstruction from Multi-Modal Data

We propose a probabilistic generative model for inferring semantically-informed aerial reconstructions from multi-modal data within a consistent mathematical framework. The approach, called Semantically Aware Aerial Reconstruction (SAAR), not only exploits inferred scene geometry, appearance, and semantic observations to obtain a meaningful categorization of the data, but also extends previously proposed methods by imposing structure on the prior over geometry, appearance, and semantic labels.

Bibtex

@inproceedings{Cabezas2015semantics,
  author={Cabezas, Randi and Straub, Julian and Fisher III, John W.}, 
  booktitle = {International Conference on Computer Vision (ICCV)},
  title=,
  year={2015},
  month = {December},
  url = {http://people.csail.mit.edu/rcabezas/pubs/cabezas15_semantics.pdf}
}