CSFlow: 自動運転向けクロスストリップ相関によるオプティカルフロー学習
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving
自動運転シーン向けに、画像をストリップ状に分割して大域的な文脈を相関ボリュームに符号化するCSCモジュールと、フロー初期化に大域文脈を活用するCRIモジュールからなるオプティカルフロー推定ネットワークを提案し、KITTI-2015で最高精度を達成した。
著者: Hao Shi, Yifan Zhou, Kailun Yang, Xiaoting Yin, Kaiwei Wang
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
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.
