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

The ability to perform pixel-wise semantic segmentation in real-time is of
paramount importance in mobile applications. Recent deep neural networks aimed
at this task have the disadvantage of requiring a large number of floating
point operations and have long run-times that hinder their usability. In this
paper, we propose a novel deep neural network architecture named ENet
(efficient neural network), created specifically for tasks requiring low
latency operation. ENet is up to 18$\times$ faster, requires 75$\times$ less
FLOPs, has 79$\times$ less parameters, and provides similar or better accuracy
to existing models. We have tested it on CamVid, Cityscapes and SUN datasets
and report on comparisons with existing state-of-the-art methods, and the
trade-offs between accuracy and processing time of a network. We present
performance measurements of the proposed architecture on embedded systems and
suggest possible software improvements that could make ENet even faster.

本篇论文提出一种名为 ENet 的新型深度神经网络架构，其最大特点是快速、参数少，适用于对实时性要求较高的像素级语义分割任务，且在 CamVid、Cityscapes 和 SUN 等数据集上测试表明效果相对优于同类型已有网络。