TY - GEN
T1 - Real-time dense geometry from a handheld camera
AU - Stühmer, Jan
AU - Gumhold, Stefan
AU - Cremers, Daniel
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78349284463&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15986-2_2
DO - 10.1007/978-3-642-15986-2_2
M3 - Conference contribution
AN - SCOPUS:78349284463
SN - 3642159850
SN - 9783642159855
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 11
EP - 20
BT - Pattern Recognition - 32nd DAGM Symposium, Proceedings
T2 - 32nd Annual Symposium of the German Association for Pattern Recognition, DAGM 2010
Y2 - 22 September 2010 through 24 September 2010
ER -