UniDAC achieves universal metric depth estimation across camera types by decoupling relative depth prediction from spatially varying scale estimation using a depth-guided module and distortion-aware positional embedding.
Depthlab: Real-time 3d in- teraction with depth maps for mobile augmented reality
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
RAD retrieves semantically similar RGB-D context samples for low-confidence regions and fuses them via matched cross-attention to cut relative absolute depth error by 29.2% on NYU Depth v2 underrepresented classes while staying competitive on standard benchmarks.
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UniDAC: Universal Metric Depth Estimation for Any Camera
UniDAC achieves universal metric depth estimation across camera types by decoupling relative depth prediction from spatially varying scale estimation using a depth-guided module and distortion-aware positional embedding.
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RAD: Retrieval-Augmented Monocular Metric Depth Estimation for Underrepresented Classes
RAD retrieves semantically similar RGB-D context samples for low-confidence regions and fuses them via matched cross-attention to cut relative absolute depth error by 29.2% on NYU Depth v2 underrepresented classes while staying competitive on standard benchmarks.