ICCV 2025poster0 citations

PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View

Longliang Liu, Miaojie Feng, Junda Cheng, Jijun Xiang, Xuan Zhu, Xin Yang

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 of the primitive branch, 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.

BibTeX
@InProceedings{Liu_2025_ICCV,
    author    = {Liu, Longliang and Feng, Miaojie and Cheng, Junda and Xiang, Jijun and Zhu, Xuan and Yang, Xin},
    title     = {PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2025},
    pages     = {5326-5336}
}
PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View · ICCV 2025