Abstract
Estimating 2D camera motion is a fundamental computer vision task that models the projection of 3D camera movements onto the 2D image plane. Current methods rely on either homography-based approaches, limited to planar scenes, or meshflow techniques that use grid-based local homographies but struggle with complex non-linear transformations. A key insight of our work is that combining flow fields from different homographies creates motion patterns that cannot be represented by any single homography. We introduce CamFlow, a novel framework that represents camera motion using hybrid motion bases: physical bases derived from camera geometry and stochastic bases for complex scenarios. Our approach includes a hybrid probabilistic loss function based on the Laplace distribution that enhances training robustness. For evaluation, we create a new benchmark by masking dynamic objects in existing optical flow datasets to isolate pure camera motion. Experiments show CamFlow outperforms state-of-the-art methods across diverse scenarios, demonstrating superior robustness and generalization in zero-shot settings. Code and datasets are available at our project page: this https URL.
Abstract (translated)
估计2D相机运动是计算机视觉中的一个基本任务，它模拟了3D相机移动在2D图像平面上的投影。目前的方法依赖于基于齐次变换的方法（仅限于平面场景）或使用网格局部齐次变换的网流技术，但后者难以处理复杂的非线性转换。我们工作的一个关键见解是，结合来自不同齐次变换的流动场可以创建出任何单一齐次变换都无法表示的运动模式。为此，我们提出了CamFlow，这是一个新的框架，它利用混合运动基来表示相机运动：物理基来源于相机几何学，而随机基用于处理复杂场景。我们的方法包括一个基于拉普拉斯分布的混合概率损失函数，以增强训练过程中的鲁棒性。为了评估性能，我们在现有的光流数据集中遮蔽动态对象，从而创建了一个新的基准测试来隔离纯粹的相机运动。实验结果显示，在各种不同场景下，CamFlow均优于当前最先进的方法，并在无模型设置中展示了更强的稳健性和泛化能力。该项目的代码和数据集可在项目页面上获取：此链接。
URL
https://arxiv.org/abs/2507.22480
