Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAM. (arXiv:2204.13877v1 [cs.CV])

Feature-based visual simultaneous localization and mapping (SLAM) methods
only estimate the depth of extracted features, generating a sparse depth map.
To solve this sparsity problem, depth completion tasks that estimate a dense
depth from a sparse depth have gained significant importance in robotic
applications like exploration. Existing methodologies that use sparse depth
from visual SLAM mainly employ point features. However, point features have
limitations in preserving structural regularities owing to texture-less
environments and sparsity problems. To deal with these issues, …

arxiv cv mesh slam unsupervised visual slam