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.

本文提出一种基于 ENet 的实时高效实现，可以同时解决语义场景分割、实例分割和单目深度估计三个自动驾驶相关任务，不会因同时运行多个任务而牺牲准确性。

自动驾驶快速场景理解

Fast Scene Understanding for Autonomous Driving

Recent approaches for instance-aware semantic labeling have augmented
convolutional neural networks (CNNs) with complex multi-task architectures or
computationally expensive graphical models. We present a method that leverages
a fully convolutional network (FCN) to predict semantic labels, depth and an
instance-based encoding using each pixel's direction towards its corresponding
instance center. Subsequently, we apply low-level computer vision techniques to
generate state-of-the-art instance segmentation on the street scene datasets
KITTI and Cityscapes. Our approach outperforms existing works by a large margin
and can additionally predict absolute distances of individual instances from a
monocular image as well as a pixel-level semantic labeling.

本文进行了实例感知语义分割的研究，提出了一种利用全卷积网络的方法预测语义标签、深度和基于实例的编码，并通过低层次计算机视觉技术在 KITTI 和 Cityscapes 街景数据集上生成最先进的实例分割，该方法在性能上超过了现有的工作，并可以额外预测单个实例与单眼图像的绝对距离以及像素级语义标签。