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

2026-07-09

AI Summary

This work addresses the limited cross-domain generalization of existing lightweight monocular depth estimation models and the deployment challenges of high-accuracy foundation models on resource-constrained devices. To bridge this gap, we propose ZipDepth, which introduces large-scale multi-domain knowledge distillation into a compact architecture for the first time, coupled with an efficient reparameterizable encoder-decoder design. With only 6.1 million parameters, ZipDepth substantially narrows the accuracy gap with large models while achieving state-of-the-art zero-shot cross-domain performance across five benchmarks. The method strikes an optimal balance between generalization and inference efficiency, enabling real-time deployment ranging from server-grade GPUs to low-power edge devices.

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.

Problem

• monocular depth estimation
• zero-shot generalization
• lightweight models
• domain shift
• efficient deployment

Innovation

• monocular depth estimation
• zero-shot generalization
• knowledge distillation
• lightweight neural network
• domain robustness