@InProceedings{DanielHernandez2017,
author="Daniel Hernandez
and Lukas Schneider
and Antonio Espinosa
and David Vazquez
and Antonio Lopez
and Uwe Franke
and Marc Pollefeys
and Juan C. Moure",
title="Slanted Stixels: Representing San Francisco{\textquoteright}s Steepest Streets\}",
booktitle="28th British Machine Vision Conference",
year="2017",
abstract="In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.",
optnote="ADAS; 600.118",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2945), last updated on Fri, 21 Jan 2022 11:19:20 +0100",
file=":http://refbase.cvc.uab.es/files/HSE2017a.pdf:PDF"
}