ArXiv

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

2026-07-09 · 17:59 UTC ·Fabio Tosi, Luca Bartolomei, Matteo Poggi... ·1 min read
Authors
Fabio Tosi, Luca Bartolomei, Matteo Poggi...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2607.08771v1
PDF
https://arxiv.org/pdf/2607.08771v1

Brief

ZipDepth is a 6.1M-parameter monocular depth model that combines an efficient reparameterizable encoder–decoder with large-scale distillation from a foundation model trained on multi-domain data. According to the abstract, it achieves the best zero-shot accuracy/efficiency trade-off among lightweight methods across five benchmarks, runs in real time on devices, and closes much of the gap to foundation models that have roughly 50× more parameters. Full paper and code links provided on arXiv and GitHub.

Why it matters

ZipDepth is a compact monocular depth network with 6.1M parameters that uses a reparameterizable encoder–decoder and large-scale knowledge distillation from a foundation model over a multi-domain training set.

Key details

  • ZipDepth runs at real-time rates from server GPUs to power-constrained devices and achieves the best trade-off between zero-shot accuracy and deployment efficiency among lightweight models across five benchmarks, narrowing the gap to foundation models with ~50× more parameters.
  • Paper: Fabio Tosi, Luca Bartolomei, Matteo Poggi, Stefano Mattoccia; accepted to ECCV 2026. Code and project page: https://github.com/fabiotosi92/ZipDepth and https://zipdepth.github.io/ (abstract and PDF on arXiv).
Source evidence

Abstract

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.

Comment: ECCV 2026. Code: https://github.com/fabiotosi92/ZipDepth - Project page: https://zipdepth.github.io/