Depth cameras are frequently used in robotic manipulation, e.g. for visual
servoing. The quality of small and compact depth cameras is though often not
sufficient for depth reconstruction, which is required for precise tracking in
and perception of the robot's working space. Based on the work of Shabanov et
al. (2021), in this work, we present a self-supervised multi-object depth
denoising pipeline, that uses depth maps of higher-quality sensors as
close-to-ground-truth supervisory signals to denoise depth maps coming from a
lower-quality sensor. We display a computationally efficient way to align sets
of two frame pairs in space and retrieve a frame-based multi-object mask, in
order to receive a clean labeled dataset to train a denoising neural network
on. The implementation of our presented work can be found at
this https URL

本研究提出了自监督多物体深度去噪管道，使用更高质量传感器的深度图作为近似的监督信号，来去噪来自低质量传感器的深度图，并演示了一种计算有效的方法来对齐两个帧对并检索基于帧的多物体掩码，以获得干净的标记数据集以训练去噪神经网络。