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This work presents and evaluates a novel compact scene representation basedon Stixels that infers geometric and semantic information. Our approachovercomes the previous rather restrictive geometric assumptions for Stixels byintroducing a novel depth model to account for non-flat roads and slantedobjects. Both semantic and depth cues are used jointly to infer the scenerepresentation in a sound global energy minimization formulation.  Furthermore, a novel approximation scheme is introduced in order tosignificantly reduce the computational complexity of the Stixel algorithm, andthen achieve real-time computation capabilities. The idea is to first performan over-segmentation of the image, discarding the unlikely Stixel cuts, andapply the algorithm only on the remaining Stixel cuts. This work presents anovel over-segmentation strategy based on a Fully Convolutional Network (FCN),which outperforms an approach based on using local extrema of the disparitymap.  We evaluate the proposed methods in terms of semantic and geometric accuracyas well as run-time on four publicly available benchmark datasets. Our approachmaintains accuracy on flat road scene datasets while improving substantially ona novel non-flat road dataset.

Slanted Stixels: A way to represent steep streets