TY - GEN
T1 - Efficient dense scene flow from sparse or dense stereo data
AU - Wedel, Andreas
AU - Rabe, Clemens
AU - Vaudrey, Tobi
AU - Brox, Thomas
AU - Franke, Uwe
AU - Cremers, Daniel
PY - 2008
Y1 - 2008
N2 - This paper presents a technique for estimating the three-dimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. We enforce the scene flow to yield consistent displacement vectors in the left and right images. The decoupling strategy has two main advantages: Firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. The approach provides dense velocity estimates with accurate results at distances up to 50 meters.
AB - This paper presents a technique for estimating the three-dimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. We enforce the scene flow to yield consistent displacement vectors in the left and right images. The decoupling strategy has two main advantages: Firstly, we are independent in choosing a disparity estimation technique, which can yield either sparse or dense correspondences, and secondly, we can achieve frame rates of 5 fps on standard consumer hardware. The approach provides dense velocity estimates with accurate results at distances up to 50 meters.
UR - https://www.scopus.com/pages/publications/56749107057
U2 - 10.1007/978-3-540-88682-2_56
DO - 10.1007/978-3-540-88682-2_56
M3 - Conference contribution
AN - SCOPUS:56749107057
SN - 3540886818
SN - 9783540886815
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 739
EP - 751
BT - Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PB - Springer Verlag
T2 - 10th European Conference on Computer Vision, ECCV 2008
Y2 - 12 October 2008 through 18 October 2008
ER -