Multi-Object Self-Supervised Depth Denoising
Claudius Kienle David Petri
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
https://github.com/alr-internship/self-supervised-depth-denoising.