  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, we perform depth
completion with visual SLAM using line features, which can better contain
structural regularities than point features. The proposed methodology creates a
convex hull region by performing constrained Delaunay triangulation with depth
interpolation using line features. However, the generated depth includes
low-frequency information and is discontinuous at the convex hull boundary.
Therefore, we propose a mesh depth refinement (MDR) module to address this
problem. The MDR module effectively transfers the high-frequency details of an
input image to the interpolated depth and plays a vital role in bridging the
conventional and deep learning-based approaches. The Struct-MDC outperforms
other state-of-the-art algorithms on public and our custom datasets, and even
outperforms supervised methodologies for some metrics. In addition, the
effectiveness of the proposed MDR module is verified by a rigorous ablation
study.
