We propose a layered street view model to encode both depth and semantic information on street view images for autonomous driving. Recently, stixels, stix-mantics, and tiered scene labeling methods have been proposed to model street view images. We propose a 4-layer street view model, a compact representation over the recently proposed stix-mantics model. Our layers encode semantic classes like ground, pedestrians, vehicles, buildings, and sky in addition to the depths. The only input to our algorithm is a pair of stereo images. We use a deep neural network to extract the appearance features for semantic classes. We use a simple and an efficient inference algorithm to jointly estimate both semantic classes and layered depth values. Our method outperforms other competing approaches in Daimler urban scene segmentation dataset. Our algorithm is massively parallelizable, allowing a GPU implementation with a processing speed about 9 fps.
Stix-mantics @cite_11 , a recently introduced model, gives more flexibility compared to stixels. Instead of having only one stixel for every column, they allow multiple segments along every column in the image and also combine nearby segments to form superpixel-style entities with better geometric meaning. Using these stixel-inspired superpixels, semantic class labeling is addressed.
@cite_11
100686880
In this paper we present Stixmantics, a novel medium-level scene representation for real-time visual semantic scene understanding. Relevant scene structure, motion and object class information is encoded using so-called Stixels as primitive elements. Sparse feature-point trajectories are used to estimate the 3D motion field and to enforce temporal consistency of semantic labels. Spatial label coherency is obtained by using a CRF framework.