Self-Supervised Depth Completion for Active Stereo. (arXiv:2110.03234v2 [cs.CV] UPDATED)

Active stereo systems are used in many robotic applications that require 3D
information. These depth sensors, however, suffer from stereo artefacts and do
not provide dense depth estimates.In this work, we present the first
self-supervised depth completion method for active stereo systems that predicts
accurate dense depth maps. Our system leverages a feature-based visual inertial
SLAM system to produce motion estimates and accurate (but sparse) 3D landmarks.
The 3D landmarks are used both as model input and as supervision during …

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