I-RAFT: Optical Flow Estimation Model Based on Multi-scale Initialization Strategy

Shunpan Liang, Xirui Zhang, Yulei Hou

Abstract: Optical flow estimation is a fundamental task in the field of computer vision, and recent advancements in deep learning networks have led to significant performance improvements. However, existing models that employ recurrent neural networks to update optical flow from an initial value of 0 suffer from issues of instability and slow training. To address this, we propose a simple yet effective optical flow initialization module as part of the optical flow initialization stage, leading to the development of an optical flow estimation model named I-RAFT. Our approach draws inspiration from other successful algorithms in computer vision to tackle the multi-scale problem. By ting initial optical flow values from the 4D cost volume and employing a voting module, we achieve initialization. Importantly, the initialization module can be seamlessly integrated into other optical flow estimation models. Additionally, we introduce a novel multi-scale extraction module for capturing context features. Extensive experiments demonstrate the simplicity and effectiveness of our proposed model, with I-RAFT achieving state-of-the-art performance on the Sintel dataset and the second-best performance on the KITTI dataset. Remarkably, our model achieves these results with a 24.48% reduction in parameters compared to the previous state-of-the-art MatchFlow model. We have made our code publicly available to facilitate further research and development (https://github.com/zhangxirui-1997/I-RAFT).