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

• URL: http://arxiv.org/abs/2607.08771v1
• Date: Thu, 09 Jul 2026 17:59:58 GMT
• Title: ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device
• Abstract summary: We present ZipDepth, a compact monocular depth network that bridges the gap between single-domain, self-supervised paradigms and lightweight alternatives.<n>ZipDepth runs at real-time rates from server GPU to power-constrained devices, achieving the best trade-off between zero-shot accuracy and deployment efficiency.
• Score: 51.39111895181487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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