by Jan Stühmer, Stefan Gumhold, Daniel Cremers
Abstract:
We present a novel variational approach to estimate dense depth maps from multiple images in real-time. By using robust penalizers for both data term and regularizer, our method preserves discontinuities in the depth map. We demonstrate that the integration of multiple images substantially increases the robustness of estimated depth maps to noise in the input images. The integration of our method into recently published algorithms for camera tracking allows dense geometry reconstruction in real-time using a single handheld camera. We demonstrate the performance of our algorithm with real-world data.
Reference:
Real-time dense geometry from a handheld camera (Jan Stühmer, Stefan Gumhold, Daniel Cremers), In Proceedings of the 32nd DAGM conference on Pattern recognition, 2010.
Bibtex Entry:
@inproceedings{Stuhmer-2010-RTG,
author = {St\"uhmer, Jan and Gumhold, Stefan and Cremers, Daniel},
affiliations = {CGV},
areas = {areasa, areasu},
booktitle = {Proceedings of the 32nd DAGM conference on Pattern recognition},
pages = {11--20},
title = {{Real-time} dense geometry from a handheld camera},
year = {2010},
keywords = {3d-reconstruction},
abstract={We present a novel variational approach to estimate dense
depth maps from multiple images in real-time. By using robust penalizers
for both data term and regularizer, our method preserves discontinuities
in the depth map. We demonstrate that the integration of multiple images substantially increases the robustness of estimated depth maps to
noise in the input images. The integration of our method into recently
published algorithms for camera tracking allows dense geometry reconstruction in real-time using a single handheld camera. We demonstrate
the performance of our algorithm with real-world data.},
doi = {10.1007/978-3-642-15986-2_2},
url = {http://link.springer.com/chapter/10.1007%2F978-3-642-15986-2_2}
}