Abstract
Panoramic optical flow enables a comprehensive understanding of temporal dynamics across wide fields of view. However, severe distortions caused by sphere-to-plane projections, such as the equirectangular projection (ERP), significantly degrade the performance of conventional perspective-based optical flow methods, especially in polar regions. To address this challenge, we propose PriOr-Flow, a novel dual-branch framework that leverages the low-distortion nature of the orthogonal view to enhance optical flow estimation in these regions. Specifically, we introduce the Dual-Cost Collaborative Lookup (DCCL) operator, which jointly retrieves correlation information from both the primitive and orthogonal cost volumes, effectively mitigating distortion noise during cost volume construction. Furthermore, our Ortho-Driven Distortion Compensation (ODDC) module iteratively refines motion features from both branches, further suppressing polar distortions. Extensive experiments demonstrate that PriOr-Flow is compatible with various perspective-based iterative optical flow methods and consistently achieves state-of-the-art performance on publicly available panoramic optical flow datasets, setting a new benchmark for wide-field motion estimation. The code is publicly available at: this https URL.
Abstract (translated)
全景光学流能够实现对宽视野中时间动态的全面理解。然而，由球面到平面投影（如等矩形投影(ERP)）引起的严重失真会显著降低传统基于透视的方法在光学流估计中的性能，特别是在极地区域。为了解决这一挑战，我们提出了PriOr-Flow，这是一种新颖的双分支框架，利用正交视图低畸变的特点来增强这些区域内的光学流估算。具体而言，我们引入了双重成本协作查找(DCCL)操作符，它从原始和正交成本体中联合检索相关信息，在构建成本体积时有效地减少了失真噪声。此外，我们的正交驱动失真补偿(ODDC)模块迭代地细化来自两个分支的运动特征，进一步抑制极区失真。广泛的实验表明，PriOr-Flow与各种基于透视的迭代光学流方法兼容，并在公开可用的全景光学流数据集中持续表现出最先进的性能，为宽视野运动估算设立了新的基准。代码可在此网址获取：this https URL.
URL
https://arxiv.org/abs/2506.23897
