%0 Conference Proceedings
%T Vision-based Offline-Online Perception Paradigm for Autonomous Driving
%A German Ros
%A Sebastian Ramos
%A Manuel Granados
%A Amir Bakhtiary
%A David Vazquez
%A Antonio Lopez
%B IEEE Winter Conference on Applications of Computer Vision
%D 2015
%F German Ros2015
%O ADAS; 600.076
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2499), last updated on Tue, 25 Feb 2020 09:51:31 +0100
%X Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.
%K Autonomous Driving
%K Scene Understanding
%K SLAM
%K Semantic Segmentation
%U http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7045624
%U http://refbase.cvc.uab.es/files/rrg2015.pdf
%U http://dx.doi.org/10.1109/WACV.2015.38
%P 231-238