2017/08/08 by Davy Neven, Neven, Davy, Bert De Brabandere +7 · 7 citations
Computer Science · #Advanced Neural Network Applications #Advanced Image and Video Retrieval Techniques #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.1708.02550
Most approaches for instance-aware semantic labeling traditionally focus on accuracy. Other aspects like runtime and memory footprint are arguably as important for real-time applications such as autonomous driving. Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture, in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once: semantic scene segmentation, instance segmentation and monocular depth estimation. Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks. The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately.