Current optical flow and point-tracking methods rely heavily on synthetic datasets. Event cameras are novel vision sensors with advantages in challenging visual conditions, but state-of-the-art frame-based methods cannot be easily adapted to event data due to the limitations of current event simulators. We introduce a novel self-supervised loss combining the Contrast Maximization framework with a non-linear motion prior in the form of pixel-level trajectories and propose an efficient solution to solve the high-dimensional assignment problem between non-linear trajectories and events. Their effectiveness is demonstrated in two scenarios: In dense continuous-time motion estimation, our method improves the zero-shot performance of a synthetically trained model on the real-world dataset EVIMO2 by 29%. In optical flow estimation, our method elevates a simple UNet to achieve state-of-the-art performance among self-supervised methods on the DSEC optical flow benchmark. Our code is available at https://github.com/tub-rip/MotionPriorCMax.

本研究提出了一种新的自监督损失方法，将对比最大化框架与非线性运动先验相结合，形成像素级轨迹，并提出了一种有效的解决非线性轨迹和事件之间的高维赋值问题的方法。在两个场景中证明了其有效性：在密集连续时间运动估计中，我们的方法在实际数据集EVIMO2上提高了合成训练模型的零样本性能29%。在光流估计中，我们的方法将简单的UNet提升到在DSEC光流基准测试中达到了最先进的性能。

稠密连续时间运动估计的运动先验对比度最大化