Existing work on scene flow estimation focuses on autonomous driving and mobile robotics, while automated solutions are lacking for motion in nature, such as that exhibited by debris flows. We propose DEFLOW, a model for 3D motion estimation of debris flows, together with a newly captured dataset. We adopt a novel multi-level sensor fusion architecture and self-supervision to incorporate the inductive biases of the scene. We further adopt a multi-frame temporal processing module to enable flow speed estimation over time. Our model achieves state-of-the-art optical flow and depth estimation on our dataset, and fully automates the motion estimation for debris flows. The source code and dataset are available at project page.

翻译：现有场景流估计研究主要集中在自动驾驶和移动机器人领域，而针对自然界（如泥石流）中的运动尚无自动化解决方案。我们提出DEFLOW——一种用于泥石流三维运动估计的模型，并配套发布了新采集的数据集。我们采用新型多级传感器融合架构与自监督方法，融入场景的归纳偏置，同时引入多帧时序处理模块以实现随时间变化的流速估计。该模型在数据集上达到了光流与深度估计的最佳性能，并实现了泥石流运动估计的全自动化。源代码与数据集已发布于项目页面。