Paper Detail
Fabio Tosi, Luca Bartolomei, Matteo Poggi, Stefano Mattoccia
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.
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@article{tosi2026zipdepth,
title = {ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device},
author = {Fabio Tosi and Luca Bartolomei and Matteo Poggi and Stefano Mattoccia},
year = {2026},
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 reparameteriz},
url = {https://arxiv.org/abs/2607.08771},
keywords = {cs.CV},
eprint = {2607.08771},
archiveprefix = {arXiv},
}
{}