HMAFlow: Learning More Accurate Optical Flow via Hierarchical Motion Field Alignment

• URL: http://arxiv.org/abs/2409.05531v2
• Date: Sun, 15 Sep 2024 06:37:55 GMT
• Title: HMAFlow: Learning More Accurate Optical Flow via Hierarchical Motion Field Alignment
• Authors: Dianbo Ma, Kousuke Imamura, Ziyan Gao, Xiangjie Wang, Satoshi Yamane,
• Abstract summary: We present a novel method, dubbed HMAFlow, to improve optical flow estimation in challenging scenes. The proposed model mainly consists of two core components: a Hierarchical Motion Field Alignment (HMA) module and a Correlation Self-Attention (CSA) module. Experimental results demonstrate that our model achieves the best generalization performance compared to other state-of-the-art methods.
• Score: 0.5825410941577593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optical flow estimation is a fundamental and long-standing visual task. In this work, we present a novel method, dubbed HMAFlow, to improve optical flow estimation in challenging scenes, particularly those involving small objects. The proposed model mainly consists of two core components: a Hierarchical Motion Field Alignment (HMA) module and a Correlation Self-Attention (CSA) module. In addition, we rebuild 4D cost volumes by employing a Multi-Scale Correlation Search (MCS) layer and replacing average pooling in common cost volumes with a search strategy utilizing multiple search ranges. Experimental results demonstrate that our model achieves the best generalization performance compared to other state-of-the-art methods. Specifically, compared with RAFT, our method achieves relative error reductions of 14.2% and 3.4% on the clean pass and final pass of the Sintel online benchmark, respectively. On the KITTI test benchmark, HMAFlow surpasses RAFT and GMA in the Fl-all metric by relative margins of 6.8% and 7.7%, respectively. To facilitate future research, our code will be made available at https://github.com/BooTurbo/HMAFlow.

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