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Found 3 papers, 2 papers with code
METER: a mobile vision transformer architecture for monocular depth estimation
State of the art MDE models typically rely on vision transformers (ViT) architectures that are highly deep and complex, making them unsuitable for fast inference on devices with hardware constraints.
D4D: An RGBD diffusion model to boost monocular depth estimation
Ground-truth RGBD data are fundamental for a wide range of computer vision applications; however, those labeled samples are difficult to collect and time-consuming to produce.
(DE)^2 CO: Deep Depth Colorization
A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping preserves the richness of depth information better than current hand-crafted approaches.
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