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 these tough scenes, especially with 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 an search strategy using multiple search ranges. Experimental results demonstrate that our model achieves the best generalization performance in comparison 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 a relative margin of 6.8% and 7.7%, respectively. To facilitate future research, our code will be made available at this https URL.
Abstract (translated)
光束追踪估计是一个基本且长期的光学任务。在这项工作中,我们提出了一种名为HMAFlow的新方法,以提高在复杂场景中光学流估计的性能,特别是对于小物体。所提出的模型主要由两个核心组件组成:分层运动场对齐(HMA)模块和关联自注意(CSA)模块。此外,我们通过采用多尺度相关搜索(MCS)层并使用多个搜索范围来替换常见成本卷中的平均池化,从而重构了4D成本体积。实验结果表明,与最先进的其他方法相比,我们的模型具有最佳的一般化性能。具体来说,与RAFT相比,我们的方法在Sintel在线基准的干净通过和最终通过分别实现了14.2%和3.4%的相对误差减少。在KITTI测试基准上,HMAFlow在Fl-all指标上超过了RAFT和GMA,相对优势分别为6.8%和7.7%。为了促进未来的研究,我们的代码将在此处链接的URL上公开。
URL
https://arxiv.org/abs/2409.05531