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Found 3 papers, 3 papers with code
Monocular Depth Distribution Alignment with Low Computation
In this paper, we model the majority of accuracy contrast between them as the difference of depth distribution, which we call "Distribution drift".
Fast Road Segmentation via Uncertainty-aware Symmetric Network
The high performance of RGB-D based road segmentation methods contrasts with their rare application in commercial autonomous driving, which is owing to two reasons: 1) the prior methods cannot achieve high inference speed and high accuracy in both ways; 2) the different properties of RGB and depth data are not well-exploited, limiting the reliability of predicted road.
Boundary-induced and scene-aggregated network for monocular depth prediction
However, two issues remain unresolved: (1) The deep feature encodes the wrong farthest region in a scene, which leads to a distorted 3D structure of the predicted depth; (2) The low-level features are insufficient utilized, which makes it even harder to estimate the depth near the edge with sudden depth change.
