CSFlow：用于自动驾驶的交叉条带相关光流学习算法
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving
摘要：
光流估计是自动驾驶系统中的一项重要任务，有助于自动驾驶车辆感知周围场景的时间连续性信息。在现有的光流估计方法中，全对相关的计算起着重要的作用。然而，在复杂的街道场景下，对局部知识的依赖往往限制了模型的准确性。本文提出了一种新的用于自动驾驶中光流估计的深度网络架构--CSFlow，它由两个新颖的模块组成：交叉条带相关模块（CSC）和相关回归模块（CRI）。CSC利用跨目标图像和参与图像的条带化操作将全局上下文编码到相关卷中，同时保持高效率。CRI用于最大限度地利用全局上下文进行光流初始化。我们的方法在公共自动驾驶数据集KITTI-2015上达到了最先进的精度。代码可在https://github.com/MasterHow/CSFlow上公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
