ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device
Monocular depth estimation has seen remarkable progress through foundation models achieving robust zero-shot generalization, yet their computational demands place them far beyond the reach of embedded and mobile platforms. Lightweight alternatives exist, but have been developed almost exclusively within single-domain, self-supervised paradigms, failing silently under domain shift. We present ZipDepth, a compact monocular depth network that bridges this gap by combining an efficient reparameterizable encoder-decoder with large-scale knowledge distillation from a foundation model over a large mu
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- PossiblePossibly related (embedding) · 50%open-edge-platform/geti →
- LinkedLinked via arxiv author · 85%Fabio Tosi →
“ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device”
- LinkedLinked via arxiv author · 85%Luca Bartolomei →
“ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device”
- LinkedLinked via arxiv author · 85%Matteo Poggi →
“ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device”
- LinkedLinked via arxiv author · 85%Stefano Mattoccia →
“ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device”
- PossiblePossibly related (embedding) · 47%roboflow/notebooks →