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Displaying 1-4 out of 4 total
3D Traffic Scene Understanding From Movable Platforms
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Andreas Geiger,Martin Lauer,Christian Wojek,Christoph Stiller,Raquel Urtasun
Issue Date:May 2014
pp. 1-1
In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particu...
Understanding High-Level Semantics by Modeling Traffic Patterns
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Hongyi Zhang,Andreas Geiger,Raquel Urtasun
Issue Date:December 2013
pp. 3056-3063
In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects pres...
Lost! Leveraging the Crowd for Probabilistic Visual Self-Localization
Found in: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
By Marcus A. Brubaker,Andreas Geiger,Raquel Urtasun
Issue Date:June 2013
pp. 3057-3064
In this paper we propose an affordable solution to self-localization, which utilizes visual odometry and road maps as the only inputs. To this end, we present a probabilistic model as well as an efficient approximate inference algorithm, which is able to u...
An all-in-one solution to geometric and photometric calibration
Found in: Mixed and Augmented Reality, IEEE / ACM International Symposium on
By Julien Pilet, Andreas Geiger, Pascal Lagger, Vincent Lepetit, Pascal Fua
Issue Date:October 2006
pp. 69-78
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