论文标题
飞行时间的弱监督光流估计
Weakly-Supervised Optical Flow Estimation for Time-of-Flight
论文作者
论文摘要
间接飞行时间（ITOF）摄像机是3D传感器的广泛类型，它执行多个捕获以获得捕获场景的深度值。虽然在消除多条路径干扰和传感器噪声时，最近采用校正ITOF的方法实现了高性能，但几乎没有研究以解决运动伪像。在这项工作中，我们提出了一种培训算法，该算法允许直接在重建深度上监督网络的光流，而无需拥有地面真相流。我们证明了这种方法使网络的训练能够对齐原始的ITOF测量并补偿ITOF深度图像中的运动伪像。对单频和多频传感器以及多TAP传感器进行评估该方法，并能够超过其他运动补偿技术。
Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene. While recent approaches to correct iToF depths achieve high performance when removing multi-path-interference and sensor noise, little research has been done to tackle motion artifacts. In this work we propose a training algorithm, which allows to supervise Optical Flow (OF) networks directly on the reconstructed depth, without the need of having ground truth flows. We demonstrate that this approach enables the training of OF networks to align raw iToF measurements and compensate motion artifacts in the iToF depth images. The approach is evaluated for both single- and multi-frequency sensors as well as multi-tap sensors, and is able to outperform other motion compensation techniques.
