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

Monocular depth estimation has seen remarkable progressthrough foundation models achieving robust zero-shot generalization, yettheir computational demands place them far beyond the reach of em-bedded and mobile platforms. Lightweight alternatives exist, but havebeen developed almost exclusively within single-domain, self-supervisedparadigms, failing silently under domain shift. We present ZipDepth, acompact monocular depth network that bridges this gap by combining anefficient reparameterizable encoder-decoder with large-scale knowledgedistillation from a foundation model over a large multi-domain train-ing set. Comprising just 6.1M parameters, ZipDepth runs at real-timerates from server GPUs to power-constrained devices, achieving the besttrade-off between zero-shot accuracy and deployment efficiency amonglightweight models across five benchmarks, taking a significant step to-wards the accuracy of foundation models with 50× more parameters.