DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments

DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments

We introduce a novel deep neural network designed for fast and structurally consistent monocular 360° depth estimation in indoor settings. Our model generates a spherical depth map from a single gravity-aligned or gravity-rectified equirectangular image, ensuring the predicted depth aligns with the...

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Bibliographic Details
Main Authors: Giovanni Pintore, Marco Agus, Alberto Signoroni, Enrico Gobbetti
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Graphical Models
Subjects:
Depth estimation
Spherical images
Indoor environments
Structural consistency
Online Access:http://www.sciencedirect.com/science/article/pii/S1524070325000281
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http://www.sciencedirect.com/science/article/pii/S1524070325000281

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