CDPR integrates polarization priors into a diffusion-based monocular depth estimator via shared latent space and adaptive gating, outperforming RGB-only methods in challenging scenes.
Unsupervised monocular depth estimation using attention and multi-warp reconstruction,
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 3years
2026 3representative citing papers
LiftFormer transforms monocular depth prediction into depth-oriented geometric and edge-aware subspace representations via lifting and frame theory, achieving state-of-the-art results on standard datasets.
Monocular depth estimation is recast as indirect feature restoration via an invertible diffusion module plus auxiliary viewpoint enhancement, delivering 4-38% RMSE gains on KITTI over baselines.
citing papers explorer
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CDPR: Cross-modal Diffusion with Polarization for Reliable Monocular Depth Estimation
CDPR integrates polarization priors into a diffusion-based monocular depth estimator via shared latent space and adaptive gating, outperforming RGB-only methods in challenging scenes.
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LiftFormer: Lifting and Frame Theory Based Monocular Depth Estimation Using Depth and Edge Oriented Subspace Representation
LiftFormer transforms monocular depth prediction into depth-oriented geometric and edge-aware subspace representations via lifting and frame theory, achieving state-of-the-art results on standard datasets.
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Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach
Monocular depth estimation is recast as indirect feature restoration via an invertible diffusion module plus auxiliary viewpoint enhancement, delivering 4-38% RMSE gains on KITTI over baselines.