mlsanityData to 7 Oct 2026

ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device

arXiv 2607.08771 ↗First posted 9 Jul 2026cs.CVcited by 1

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Abstract (arXiv)

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 multi-domain training set. Comprising just 6.1M parameters, ZipDepth runs at real-time rates from server GPUs to power-constrained devices, achieving the best trade-off between zero-shot accuracy and deployment efficiency among lightweight models across five benchmarks, taking a significant step towards the accuracy of foundation models with 50x more parameters.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 80 of its 103 references that are in the map. First posted on arXiv 9 Jul 2026.

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80 of 103 references.

References · 80

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38 more
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  1. 2026GTR: Gated Token Recurrence for Efficient Dense Predictioncited by 0