论文标题
CSFLOW：通过横条相关性学习光流以自动驾驶
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving
论文作者
论文摘要
光流估计是自动驾驶系统的重要任务，这有助于自动驾驶汽车感知周围场景的时间连续性信息。在许多现有的最新光流估计方法中，全对相关的计算起着重要作用。但是，对本地知识的依赖通常会限制模型在复杂的街道场景下的准确性。在本文中，我们提出了一种新的深网架构，用于自主驾驶中的光流估计 - CSFlow，该架构由两个新型模块组成：横条相关模块（CSC）和相关回归初始化模块（CRI）。 CSC利用跨目标图像的条纹操作，并在维持高效率的同时将全局上下文编码为相关量。 CRI用于最大程度地利用全局上下文来进行光流初始化。我们的方法已在公共自动驾驶数据集Kitti-2015上实现了最先进的准确性。代码可在https://github.com/masterhow/csflow上公开获取。
Optical flow estimation is an essential task in self-driving systems, which helps autonomous vehicles perceive temporal continuity information of surrounding scenes. The calculation of all-pair correlation plays an important role in many existing state-of-the-art optical flow estimation methods. However, the reliance on local knowledge often limits the model's accuracy under complex street scenes. In this paper, we propose a new deep network architecture for optical flow estimation in autonomous driving--CSFlow, which consists of two novel modules: Cross Strip Correlation module (CSC) and Correlation Regression Initialization module (CRI). CSC utilizes a striping operation across the target image and the attended image to encode global context into correlation volumes, while maintaining high efficiency. CRI is used to maximally exploit the global context for optical flow initialization. Our method has achieved state-of-the-art accuracy on the public autonomous driving dataset KITTI-2015. Code is publicly available at https://github.com/MasterHow/CSFlow.
