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.
翻译:光线(iToF)摄像头是一种广泛的三维感应器,它进行多重捕捉,以获得所捕捉场景的深度值。虽然最近纠正iToF深度的方法在清除多路干扰和感应噪音时性能很高,但在处理运动文物方面没有做多少研究。在这项工作中,我们提议了一个培训算法,这种算法可以直接在重建的深度上监督光流网络,而不必有地面的真相流。我们证明,这种方法使网络的培训能够使原始iToF测量和补偿iToF深度图像中的运动文物。对单频和多频传感器以及多波传感器都进行了评估,并且能够超越其他运动补偿技术。