@InProceedings{GermanRos2016,
author="German Ros
and Laura Sellart
and Joanna Materzynska
and David Vazquez
and Antonio Lopez",
title="The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes",
booktitle="29th IEEE Conference on Computer Vision and Pattern Recognition",
year="2016",
pages="3234--3243",
optkeywords="Domain Adaptation",
optkeywords="Autonomous Driving",
optkeywords="Virtual Data",
optkeywords="Semantic Segmentation",
abstract="Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task",
optnote="ADAS; 600.085; 600.082; 600.076",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2739), last updated on Wed, 14 Nov 2018 13:47:20 +0100",
doi="10.1109/CVPR.2016.352",
file=":http://refbase.cvc.uab.es/files/RSM2016.pdf:PDF"
}