从单张模糊图像中推断相机三维运动的视图变换关系。
Robust Fundamental Matrix Estimation from Single Image Motion Blur
- 利用模糊轨迹内的时序对应关系，重构相机运动的基矩阵。
- 在合成与真实数据集上成功估计出编码3D运动的基矩阵。
- 适用于运动分割等下游任务，对模糊方向不确定性强鲁棒。
本文提出一项挑战性任务：从单张运动模糊图像中提取基础矩阵。当相机在曝光期间进行三维运动时，模糊图像中的拖影路径包含了该运动的线索与约束。我们证明了可在曝光窗口内建立两个时间点之间的对应关系，并据此鲁棒地推断出基础矩阵，该矩阵总结了相机在曝光期间的运动。所推断的基础矩阵在转置意义下唯一，对应于时间方向的模糊性。由于这种每条拖影路径的时间方向不确定性，经典方法如8点算法不再适用。本文提出的方法针对时间方向不确定的对应关系进行修正。为提升估计鲁棒性，还引入拖影模式预测的不确定性度量，并用于估计器的采样过程。在合成与真实世界运动模糊数据集上的实验表明，本方法可从单帧图像中准确估计出编码3D相机运动的基础矩阵。其实际应用价值在运动分割任务中得到验证。
原文摘要 · Abstract (English)
In this paper, we introduce a challenging task: extracting a fundamental matrix from a single motion blurred image. For a camera moving in 3D during exposure, the smear paths in the blurry image contain cues and constraints on this motion. We demonstrate the feasibility of establishing correspondences between two time instances within the camera exposure window, and that these can be used to robustly infer a fundamental matrix, which summarizes the motion of the camera during the exposure time. The inferred fundamental matrix is unique up to a transpose, corresponding to an ambiguity of the direction of time. Due to this per-smear ambiguity, classic methods, such as the 8-point algorithm, are no longer usable. The proposed method modifies the estimation to work on time-direction ambiguous correspondences. To improve the robustness of the fundamental matrix estimation, we also propose to incorporate an uncertainty measurement in smear pattern prediction and use it in the sampling process of the estimator. Experiments on synthetic and real-world motion-blur datasets demonstrate that our approach is able to estimate the fundamental matrix encoding the 3D camera motion, from single frames. Practical applicability is demonstrated on the downstream task of motion segmentation.
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