Range images captured by Time-of-Flight (ToF) cameras are corrupted with multipath distortions due to interaction between modulated light signals and scenes. The interaction is often complicated, which makes a model-based solution elusive. We propose a learning-based approach for removing the multipath distortions for the ToF camera in a robotic arm setup. Our approach is based on deep learning and geodesic filtering. We use the robotic arm to automatically collect a large amount of ToF range images containing various multipath distortions. The training images are automatically labeled by leveraging a high precision structured light sensor available only in the training time. In the test time, we apply the learned model to remove the multipath distortions and obtain accurate range images. This allows our robotic arm setup to enjoy the speed and compact form of the ToF camera without compromising with its range measurement errors. We conduct extensive experimental validations and compare the proposed method to several baseline algorithms. The experiment results show that our method achieves 55% error reduction in range estimation and significantly outperforms the baseline algorithms.

本研究提出了一种基于深度学习的学习方法，采用机械臂自动地采集大量的ToF范围图像，并在训练时间利用高精度结构光传感器自动标记训练图像，从而去除ToF相机中的多路径失真。在实验验证中，我们的方法实现了55％的测距误差减小，并且比其他基线算法表现更好。

学习移除飞行时间测距图像中的多路径扭曲，为机器人臂设置