Layered Interpretation of Street View Images

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.Comment: The paper will be presented in the 2015 Robotics: Science and Systems Conference (RSS

External Bias Dependent Direct To Indirect Bandgap Transition in Graphene Nanoribbon

In this work, using self-consistent tight-binding calculations, for the first time, we show that a direct to indirect bandgap transition is possible in an armchair graphene nanoribbon by the application of an external bias along the width of the ribbon, opening up the possibility of new device applications. With the help of Dirac equation, we qualitatively explain this bandgap transition using the asymmetry in the spatial distribution of the perturbation potential produced inside the nanoribbon by the external bias. This is followed by the verification of the bandgap trends with a numerical technique using Magnus expansion of matrix exponentials. Finally, we show that the carrier effective masses possess tunable sharp characters in the vicinity of the bandgap transition points.Comment: Accepted for publication in Nano Letter